2019
William Van Woensel; Samina Raza Abidi; Borna Jafarpour; Syed Sibte Raza Abidi
Providing Comorbid Decision Support via the Integration of Clinical Practice Guidelines at Execution-Time by Leveraging Medical Linked Open Datasets Proceedings Article
In: 17th World Congress on Medical and Health Informatics (MEDINFO'19), Aug 26-30, pp. 858 - 862, IOS Press, Lyon, France, 2019.
@inproceedings{VanWoensel2019a,
title = {Providing Comorbid Decision Support via the Integration of Clinical Practice Guidelines at Execution-Time by Leveraging Medical Linked Open Datasets},
author = {William Van Woensel and Samina Raza Abidi and Borna Jafarpour and Syed Sibte Raza Abidi},
url = {http://ebooks.iospress.nl/publication/52110},
doi = {10.3233/SHTI190345},
year = {2019},
date = {2019-08-26},
booktitle = {17th World Congress on Medical and Health Informatics (MEDINFO'19), Aug 26-30},
volume = {264},
pages = {858 - 862},
publisher = {IOS Press},
address = {Lyon, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Raquel da Luz Diaz; Marcela de Oliveira Lima; João G B Alves; William Van Woensel; Asil Naqvi; Zahra Take; Syed Sibte Raza Abidi
A Digital Health Platform to Deliver Tailored Early Stimulation Programs for Children With Developmental Delay Proceedings Article
In: 17th World Congress on Medical and Health Informatics (MEDINFO'19), Aug 26-30, pp. 571 - 575, IOS Press, Lyon, France, 2019.
@inproceedings{DaLuzDiaz2019,
title = {A Digital Health Platform to Deliver Tailored Early Stimulation Programs for Children With Developmental Delay},
author = {Raquel da Luz Diaz and Marcela de Oliveira Lima and João G B Alves and William Van Woensel and Asil Naqvi and Zahra Take and Syed Sibte Raza Abidi},
url = {http://ebooks.iospress.nl/publication/52052},
doi = {10.3233/SHTI190287},
year = {2019},
date = {2019-08-26},
booktitle = {17th World Congress on Medical and Health Informatics (MEDINFO'19), Aug 26-30},
volume = {264},
pages = {571 - 575},
publisher = {IOS Press},
address = {Lyon, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Benjamin Rose-Davis; William Van Woensel; Elizabeth Stringer; Samina Raza Abidi; Syed Sibte Raza Abidi
Using Artificial Intelligence-Based Argument Theory To Generate Automated Patient Education Dialogues For Families Of Children With Juvenile Idiopathic Arthritis Proceedings Article
In: 17th World Congress on Medical and Health Informatics (MEDINFO'19), Aug 26-30, pp. 1337 - 1341, Lyon, France, 2019.
@inproceedings{Rose-Davis2019,
title = {Using Artificial Intelligence-Based Argument Theory To Generate Automated Patient Education Dialogues For Families Of Children With Juvenile Idiopathic Arthritis},
author = {Benjamin Rose-Davis and William Van Woensel and Elizabeth Stringer and Samina Raza Abidi and Syed Sibte Raza Abidi},
url = {http://ebooks.iospress.nl/publication/52209},
doi = {10.3233/SHTI190444},
year = {2019},
date = {2019-08-26},
booktitle = {17th World Congress on Medical and Health Informatics (MEDINFO'19), Aug 26-30},
volume = {264},
pages = {1337 - 1341},
address = {Lyon, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Syed Sibte Raza Abidi; Jaber Rad; Ashraf Abusharekh; Patrice C. Roy; William Van Woensel; Samina Raza Abidi; Calvino Cheng; Bryan D. Crocker; Manal O. Elnenaei
AI-Driven Pathology Laboratory Utilization Management via Data- and Knowledge-Based Analytics Proceedings Article
In: 17th Conf. on Artificial Intelligence in Medicine (AIME2019), June 26-29, pp. 241–251, Springer International Publishing, Poznan, Poland, 2019, ISBN: 978-3-030-21642-9.
@inproceedings{Abidi2019,
title = {AI-Driven Pathology Laboratory Utilization Management via Data- and Knowledge-Based Analytics},
author = {Syed Sibte Raza Abidi and Jaber Rad and Ashraf Abusharekh and Patrice C. Roy and William Van Woensel and Samina Raza Abidi and Calvino Cheng and Bryan D. Crocker and Manal O. Elnenaei},
url = {https://link.springer.com/chapter/10.1007/978-3-030-21642-9_30},
doi = {10.1007/978-3-030-21642-9_30},
isbn = {978-3-030-21642-9},
year = {2019},
date = {2019-06-26},
booktitle = {17th Conf. on Artificial Intelligence in Medicine (AIME2019), June 26-29},
pages = {241--251},
publisher = {Springer International Publishing},
address = {Poznan, Poland},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Patrice C. Roy; William Van Woensel; Andy Wilcox; Syed Sibte Raza Abidi
Mobile Indoor Localization with Bluetooth Beacons in a Pediatric Emergency Department Using Clustering, Rule-based Classification and High-level Heuristics Proceedings Article
In: 17th Conf. on Artificial Intelligence in Medicine (AIME2019), June 26-29, pp. 216–226, Springer International Publishing, Poznan, Poland, 2019, ISBN: 978-3-030-21642-9.
@inproceedings{Roy2019,
title = {Mobile Indoor Localization with Bluetooth Beacons in a Pediatric Emergency Department Using Clustering, Rule-based Classification and High-level Heuristics},
author = {Patrice C. Roy and William Van Woensel and Andy Wilcox and Syed Sibte Raza Abidi},
url = {https://link.springer.com/chapter/10.1007/978-3-030-21642-9_27},
doi = {10.1007/978-3-030-21642-9_27},
isbn = {978-3-030-21642-9},
year = {2019},
date = {2019-06-26},
booktitle = {17th Conf. on Artificial Intelligence in Medicine (AIME2019), June 26-29},
pages = {216--226},
publisher = {Springer International Publishing},
address = {Poznan, Poland},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Kathryn Young-Shand; Patrice C. Roy; Syed Sibte Raza Abidi; Michael Dunbar; Janie Astephen Wilson
Clinical and Biomechanical Cluster Classification Before TKA Impacts Functional Outcome Proceedings Article
In: 2nd International Combined Meeting of Orthopaedic Research Societies, June 19-22, 2019, Montreal, Canada, 2019.
Abstract | BibTeX | Tags: Clustering, Total knee arthroplasty
@inproceedings{Young-Shand2019b,
title = {Clinical and Biomechanical Cluster Classification Before TKA Impacts Functional Outcome},
author = {Kathryn Young-Shand and Patrice C. Roy and Syed Sibte Raza Abidi and Michael Dunbar and Janie Astephen Wilson},
year = {2019},
date = {2019-06-19},
booktitle = {2nd International Combined Meeting of Orthopaedic Research Societies, June 19-22, 2019, Montreal, Canada},
abstract = {Purpose:
Identifying knee osteoarthritis (OA) patient phenotypes is relevant to assessing treatment efficacy, yet biomechanical variability has not been applied to phenotyping. This study aimed to identify demographic and gait related groups (clusters) among total knee arthroplasty (TKA) candidates, and examine inter-cluster differences in gait feature improvement post-TKA.
Method:
Knee OA patients scheduled for TKA underwent three-dimensional gait analysis one-week pre and one-year post-TKA, capturing lower-limb external ground reaction forces and kinematics using a force platform and optoelectronic motion capture. Principal component analysis was applied to frontal and sagittal knee angle and moment waveforms (n=135 pre-TKA, n=106 post-TKA), resulting in a new uncorrelated dataset of subject PCscores and PC vectors, describing major modes of variability throughout one gait cycle (0-100%). Demographics (age, gender, body mass index (BMI), gait speed), and gait angle and moment PCscores were standardized and assessed for outliers. One patient exceeding Tukey’s outer (3*IQR) fence was removed. Two-dimensional multidimensional scaling followed by k-medoids clustering was applied to scaled demographics and pre-TKA PCscores [134x15]. Number of clusters (k=2:10) were assessed by silhouette coefficients, s, and stability by Adjusted Rand Indices (ARI) of 100 data subsets. Clusters were validated by examining inter-cluster differences at baseline, and inter-cluster gait changes (PostPCscore–PrePCscore, n=105) by k-way ANOVA and Tukey's honestly significant difference (HSD) criterion.
Results:
Four (k=4) TKA candidate groups yielded optimum clustering metrics (s=0.4, ARI=0.75). Cluster 1 was all-males (male:female=19:0) who walked with faster gait speeds (1>2,3), larger flexion angle magnitudes and stance-phase angle range (PC1 & PC4 1>2,3,4), and more flexion (PC2 1>2,3,4) and adduction moment (PC2 & PC3 1>2,3) range patterns. Cluster 1 had the most dynamic kinematics and kinetic loading/unloading range amongst the clusters, representing a higher-functioning (less “stiff”) male subset. Cluster 2 captured older (2>1,3) males (31:1) with slower gait speeds (2<1,4), and less flexion moment (PC2 2<1,4) and adduction moment (PC2 2<1,4) range, describing an older, stiff-gait male subset. Cluster 3 was mostly (4:36) females with slower gait speeds (3<1,4), higher BMIs (3>4), and lower flexion angle magnitude (PC1 3<1,2,4) and flexion moment range (PC2 3<1,2,4) features. Cluster 3 showed the “stiffest” gait amongst the clusters, representing a more-obese, stiff-gait female subset. Cluster 4 was mostly (2:41) females with faster gait speeds (4>2,3) and less stiff kinematic and kinetic patterns relative to Clusters 2 and 3, representing a higher-functioning female subset. Radiographic severity did not differ between clusters (Kellgren-Lawrence Grade, p=0.9, n=102), and after removing demographics and re-clustering, gender differences remained (p<0.04). Pre-TKA, higher-functioning clusters (1&4) had more dynamic loading/un-loading kinetic patterns. Post-TKA, high-functioning clusters experienced less gait improvement (flexion angle PC2, 1,4<3, p≥0.004; flexion moment PC2, 4<2,3), with some sagittal range patterns decreasing postoperatively.
Conclusion:
TKA candidates can be characterized by four clusters, differing by demographics and biomechanical severity features. Post-TKA, functional gains were cluster-specific; stiff-gait clusters experienced more improvement, while higher-functioning clusters experienced less gain and showed some decline. Results suggest the presence of cohorts who may not benefit functionally from TKA. Cluster profiling may support triaging and developing targeted OA treatment strategies, meeting individual function needs.},
keywords = {Clustering, Total knee arthroplasty},
pubstate = {published},
tppubtype = {inproceedings}
}
Purpose:
Identifying knee osteoarthritis (OA) patient phenotypes is relevant to assessing treatment efficacy, yet biomechanical variability has not been applied to phenotyping. This study aimed to identify demographic and gait related groups (clusters) among total knee arthroplasty (TKA) candidates, and examine inter-cluster differences in gait feature improvement post-TKA.
Method:
Knee OA patients scheduled for TKA underwent three-dimensional gait analysis one-week pre and one-year post-TKA, capturing lower-limb external ground reaction forces and kinematics using a force platform and optoelectronic motion capture. Principal component analysis was applied to frontal and sagittal knee angle and moment waveforms (n=135 pre-TKA, n=106 post-TKA), resulting in a new uncorrelated dataset of subject PCscores and PC vectors, describing major modes of variability throughout one gait cycle (0-100%). Demographics (age, gender, body mass index (BMI), gait speed), and gait angle and moment PCscores were standardized and assessed for outliers. One patient exceeding Tukey’s outer (3*IQR) fence was removed. Two-dimensional multidimensional scaling followed by k-medoids clustering was applied to scaled demographics and pre-TKA PCscores [134x15]. Number of clusters (k=2:10) were assessed by silhouette coefficients, s, and stability by Adjusted Rand Indices (ARI) of 100 data subsets. Clusters were validated by examining inter-cluster differences at baseline, and inter-cluster gait changes (PostPCscore–PrePCscore, n=105) by k-way ANOVA and Tukey's honestly significant difference (HSD) criterion.
Results:
Four (k=4) TKA candidate groups yielded optimum clustering metrics (s=0.4, ARI=0.75). Cluster 1 was all-males (male:female=19:0) who walked with faster gait speeds (1>2,3), larger flexion angle magnitudes and stance-phase angle range (PC1 & PC4 1>2,3,4), and more flexion (PC2 1>2,3,4) and adduction moment (PC2 & PC3 1>2,3) range patterns. Cluster 1 had the most dynamic kinematics and kinetic loading/unloading range amongst the clusters, representing a higher-functioning (less “stiff”) male subset. Cluster 2 captured older (2>1,3) males (31:1) with slower gait speeds (2<1,4), and less flexion moment (PC2 2<1,4) and adduction moment (PC2 2<1,4) range, describing an older, stiff-gait male subset. Cluster 3 was mostly (4:36) females with slower gait speeds (3<1,4), higher BMIs (3>4), and lower flexion angle magnitude (PC1 3<1,2,4) and flexion moment range (PC2 3<1,2,4) features. Cluster 3 showed the “stiffest” gait amongst the clusters, representing a more-obese, stiff-gait female subset. Cluster 4 was mostly (2:41) females with faster gait speeds (4>2,3) and less stiff kinematic and kinetic patterns relative to Clusters 2 and 3, representing a higher-functioning female subset. Radiographic severity did not differ between clusters (Kellgren-Lawrence Grade, p=0.9, n=102), and after removing demographics and re-clustering, gender differences remained (p<0.04). Pre-TKA, higher-functioning clusters (1&4) had more dynamic loading/un-loading kinetic patterns. Post-TKA, high-functioning clusters experienced less gait improvement (flexion angle PC2, 1,4<3, p≥0.004; flexion moment PC2, 4<2,3), with some sagittal range patterns decreasing postoperatively.
Conclusion:
TKA candidates can be characterized by four clusters, differing by demographics and biomechanical severity features. Post-TKA, functional gains were cluster-specific; stiff-gait clusters experienced more improvement, while higher-functioning clusters experienced less gain and showed some decline. Results suggest the presence of cohorts who may not benefit functionally from TKA. Cluster profiling may support triaging and developing targeted OA treatment strategies, meeting individual function needs.
Identifying knee osteoarthritis (OA) patient phenotypes is relevant to assessing treatment efficacy, yet biomechanical variability has not been applied to phenotyping. This study aimed to identify demographic and gait related groups (clusters) among total knee arthroplasty (TKA) candidates, and examine inter-cluster differences in gait feature improvement post-TKA.
Method:
Knee OA patients scheduled for TKA underwent three-dimensional gait analysis one-week pre and one-year post-TKA, capturing lower-limb external ground reaction forces and kinematics using a force platform and optoelectronic motion capture. Principal component analysis was applied to frontal and sagittal knee angle and moment waveforms (n=135 pre-TKA, n=106 post-TKA), resulting in a new uncorrelated dataset of subject PCscores and PC vectors, describing major modes of variability throughout one gait cycle (0-100%). Demographics (age, gender, body mass index (BMI), gait speed), and gait angle and moment PCscores were standardized and assessed for outliers. One patient exceeding Tukey’s outer (3*IQR) fence was removed. Two-dimensional multidimensional scaling followed by k-medoids clustering was applied to scaled demographics and pre-TKA PCscores [134x15]. Number of clusters (k=2:10) were assessed by silhouette coefficients, s, and stability by Adjusted Rand Indices (ARI) of 100 data subsets. Clusters were validated by examining inter-cluster differences at baseline, and inter-cluster gait changes (PostPCscore–PrePCscore, n=105) by k-way ANOVA and Tukey's honestly significant difference (HSD) criterion.
Results:
Four (k=4) TKA candidate groups yielded optimum clustering metrics (s=0.4, ARI=0.75). Cluster 1 was all-males (male:female=19:0) who walked with faster gait speeds (1>2,3), larger flexion angle magnitudes and stance-phase angle range (PC1 & PC4 1>2,3,4), and more flexion (PC2 1>2,3,4) and adduction moment (PC2 & PC3 1>2,3) range patterns. Cluster 1 had the most dynamic kinematics and kinetic loading/unloading range amongst the clusters, representing a higher-functioning (less “stiff”) male subset. Cluster 2 captured older (2>1,3) males (31:1) with slower gait speeds (2<1,4), and less flexion moment (PC2 2<1,4) and adduction moment (PC2 2<1,4) range, describing an older, stiff-gait male subset. Cluster 3 was mostly (4:36) females with slower gait speeds (3<1,4), higher BMIs (3>4), and lower flexion angle magnitude (PC1 3<1,2,4) and flexion moment range (PC2 3<1,2,4) features. Cluster 3 showed the “stiffest” gait amongst the clusters, representing a more-obese, stiff-gait female subset. Cluster 4 was mostly (2:41) females with faster gait speeds (4>2,3) and less stiff kinematic and kinetic patterns relative to Clusters 2 and 3, representing a higher-functioning female subset. Radiographic severity did not differ between clusters (Kellgren-Lawrence Grade, p=0.9, n=102), and after removing demographics and re-clustering, gender differences remained (p<0.04). Pre-TKA, higher-functioning clusters (1&4) had more dynamic loading/un-loading kinetic patterns. Post-TKA, high-functioning clusters experienced less gait improvement (flexion angle PC2, 1,4<3, p≥0.004; flexion moment PC2, 4<2,3), with some sagittal range patterns decreasing postoperatively.
Conclusion:
TKA candidates can be characterized by four clusters, differing by demographics and biomechanical severity features. Post-TKA, functional gains were cluster-specific; stiff-gait clusters experienced more improvement, while higher-functioning clusters experienced less gain and showed some decline. Results suggest the presence of cohorts who may not benefit functionally from TKA. Cluster profiling may support triaging and developing targeted OA treatment strategies, meeting individual function needs.
Syed Sibte Raza Abidi; Samina Raza Abidi
Intelligent health data analytics: A convergence of artificial intelligence and big data Journal Article
In: Healthcare Management Forum, vol. 32, no. 4, pp. 178–182, 2019.
Abstract | Links | BibTeX | Tags:
@article{doi:10.1177/0840470419846134,
title = {Intelligent health data analytics: A convergence of artificial intelligence and big data},
author = {Syed Sibte Raza Abidi and Samina Raza Abidi},
url = {https://doi.org/10.1177/0840470419846134},
doi = {10.1177/0840470419846134},
year = {2019},
date = {2019-05-22},
journal = {Healthcare Management Forum},
volume = {32},
number = {4},
pages = {178--182},
abstract = {Healthcare is a living system that generates a significant volume of heterogeneous data. As healthcare systems are pivoting to value-based systems, intelligent and interactive analysis of health data is gaining significance for health system management, especially for resource optimization whilst improving care quality and health outcomes. Health data analytics is being influenced by new concepts and intelligent methods emanating from artificial intelligence and big data. In this article, we contextualize health data and health data analytics in terms of the emerging trends of artificial intelligence and big data. We examine the nature of health data using the big data criterion to understand “how big” is health data. Next, we explain the working of artificial intelligence–based data analytics methods and discuss “what insights” can be derived from a broad spectrum of health data analytics methods to improve health system management, health outcomes, knowledge discovery, and healthcare innovation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Healthcare is a living system that generates a significant volume of heterogeneous data. As healthcare systems are pivoting to value-based systems, intelligent and interactive analysis of health data is gaining significance for health system management, especially for resource optimization whilst improving care quality and health outcomes. Health data analytics is being influenced by new concepts and intelligent methods emanating from artificial intelligence and big data. In this article, we contextualize health data and health data analytics in terms of the emerging trends of artificial intelligence and big data. We examine the nature of health data using the big data criterion to understand “how big” is health data. Next, we explain the working of artificial intelligence–based data analytics methods and discuss “what insights” can be derived from a broad spectrum of health data analytics methods to improve health system management, health outcomes, knowledge discovery, and healthcare innovation.
Kathryn Young-Shand; Patrice C. Roy; Syed Sibte Raza Abidi; Michael Dunbar; Janie Astephen Wilson
Clinical and Biomechanical Cluster Classification Before TKA Impacts Functional Outcome Proceedings Article
In: Orthopaedic Research Society Conference (ORS), February 2-5, 2019 Austin, Texas, USA, 2019, (Recipient of the American Academy of Orthopaedic Surgeons (AAOS) Women’s Health Advisory Board (WHAB) Award).
BibTeX | Tags: Clustering, Total knee arthroplasty
@inproceedings{Young-Shand2019,
title = {Clinical and Biomechanical Cluster Classification Before TKA Impacts Functional Outcome},
author = {Kathryn Young-Shand and Patrice C. Roy and Syed Sibte Raza Abidi and Michael Dunbar and Janie Astephen Wilson},
year = {2019},
date = {2019-02-02},
booktitle = {Orthopaedic Research Society Conference (ORS), February 2-5, 2019 Austin, Texas, USA},
note = {Recipient of the American Academy of Orthopaedic Surgeons (AAOS) Women’s Health Advisory Board (WHAB) Award},
keywords = {Clustering, Total knee arthroplasty},
pubstate = {published},
tppubtype = {inproceedings}
}
Borna Jafarpour; Samina Raza Abidi; William Van Woensel; Syed Sibte Raza Abidi
Execution-Time Integration of Clinical Practice Guidelines To Provide Decision Support for Comorbid Conditions Journal Article
In: Artificial Intelligence in Medicine, vol. 94, pp. 117-137, 2019, ISSN: 0933-3657.
Abstract | Links | BibTeX | Tags:
@article{JAFARPOUR2019,
title = {Execution-Time Integration of Clinical Practice Guidelines To Provide Decision Support for Comorbid Conditions},
author = {Borna Jafarpour and Samina Raza Abidi and William Van Woensel and Syed Sibte Raza Abidi},
url = {https://authors.elsevier.com/a/1Yf0D3KEGa1e9B},
doi = {10.1016/j.artmed.2019.02.003},
issn = {0933-3657},
year = {2019},
date = {2019-01-01},
journal = {Artificial Intelligence in Medicine},
volume = {94},
pages = {117-137},
abstract = {Patients with multiple medical conditions (comorbidity) pose major challenges to clinical decision support systems, since the different Clinical Practice Guidelines (CPG) often involve adverse interactions, such as drug-drug or drug-disease interactions. Moreover, opportunities often exist for optimizing care and resources across multiple CPG. These challenges have been taken up in the state of the art, with many approaches focusing on the static integration of comorbid CIG. Nevertheless, we observe that many aspects often change dynamically over time, in ways that cannot be foreseen – such as delays in care tasks, resource availability, test outcomes, and acute comorbid conditions. To ensure the clinical safety and effectiveness of integrating multiple comorbid CIG, these execution-time difficulties must be considered. Further, when dealing with comorbid conditions, we remark that clinical practitioners typically consider multiple complex solutions, depending on the patient’s health profile. Hence, execution-time flexibility, based on dynamic health parameters, is needed to effectively and safely cope with comorbid conditions. In this work, we introduce a flexible, knowledge-driven and execution-time approach to comorbid CIG integration, based on an OWL ontology with clearly defined integration semantics.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Patients with multiple medical conditions (comorbidity) pose major challenges to clinical decision support systems, since the different Clinical Practice Guidelines (CPG) often involve adverse interactions, such as drug-drug or drug-disease interactions. Moreover, opportunities often exist for optimizing care and resources across multiple CPG. These challenges have been taken up in the state of the art, with many approaches focusing on the static integration of comorbid CIG. Nevertheless, we observe that many aspects often change dynamically over time, in ways that cannot be foreseen – such as delays in care tasks, resource availability, test outcomes, and acute comorbid conditions. To ensure the clinical safety and effectiveness of integrating multiple comorbid CIG, these execution-time difficulties must be considered. Further, when dealing with comorbid conditions, we remark that clinical practitioners typically consider multiple complex solutions, depending on the patient’s health profile. Hence, execution-time flexibility, based on dynamic health parameters, is needed to effectively and safely cope with comorbid conditions. In this work, we introduce a flexible, knowledge-driven and execution-time approach to comorbid CIG integration, based on an OWL ontology with clearly defined integration semantics.
Patrice C Roy; William Van Woensel; Andy Wilcox; Syed Sibte Raza Abidi
Mobile Indoor Localization with Bluetooth Beacons in a Pediatric Emergency Department Using Clustering, Rule-based Classification and High-level Heuristics Proceedings Article
In: 17th Conf. on Artificial Intelligence in Medicine (AIME2019), Poznan, Poland, 2019.
BibTeX | Tags:
@inproceedings{Roy2019b,
title = {Mobile Indoor Localization with Bluetooth Beacons in a Pediatric Emergency Department Using Clustering, Rule-based Classification and High-level Heuristics},
author = {Patrice C Roy and William {Van Woensel} and Andy Wilcox and Syed Sibte Raza Abidi},
year = {2019},
date = {2019-01-01},
booktitle = {17th Conf. on Artificial Intelligence in Medicine (AIME2019)},
address = {Poznan, Poland},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Hani Nabeel Mufti; Gregory Marshal Hirsch; Samina Raza Abidi; Syed Sibte Raza Abidi
In: JMIR Med Inform, vol. 7, no. 4, pp. e14993, 2019, ISSN: 2291-9694.
Abstract | Links | BibTeX | Tags: delirium; cardiac surgery; machine learning; predictive modeling
@article{info:doi/10.2196/14993,
title = {Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study},
author = {Hani Nabeel Mufti and Gregory Marshal Hirsch and Samina Raza Abidi and Syed Sibte Raza Abidi},
url = {http://www.ncbi.nlm.nih.gov/pubmed/31558433},
doi = {10.2196/14993},
issn = {2291-9694},
year = {2019},
date = {2019-01-01},
journal = {JMIR Med Inform},
volume = {7},
number = {4},
pages = {e14993},
abstract = {Background: Delirium is a temporary mental disorder that occasionally affects patients undergoing surgery, especially cardiac surgery. It is strongly associated with major adverse events, which in turn leads to increased cost and poor outcomes (eg, need for nursing home due to cognitive impairment, stroke, and death). The ability to foresee patients at risk of delirium will guide the timely initiation of multimodal preventive interventions, which will aid in reducing the burden and negative consequences associated with delirium. Several studies have focused on the prediction of delirium. However, the number of studies in cardiac surgical patients that have used machine learning methods is very limited. Objective: This study aimed to explore the application of several machine learning predictive models that can pre-emptively predict delirium in patients undergoing cardiac surgery and compare their performance. Methods: We investigated a number of machine learning methods to develop models that can predict delirium after cardiac surgery. A clinical dataset comprising over 5000 actual patients who underwent cardiac surgery in a single center was used to develop the models using logistic regression, artificial neural networks (ANN), support vector machines (SVM), Bayesian belief networks (BBN), na"ive Bayesian, random forest, and decision trees. Results: Only 507 out of 5584 patients (11.4%) developed delirium. We addressed the underlying class imbalance, using random undersampling, in the training dataset. The final prediction performance was validated on a separate test dataset. Owing to the target class imbalance, several measures were used to evaluate algorithm's performance for the delirium class on the test dataset. Out of the selected algorithms, the SVM algorithm had the best F1 score for positive cases, kappa, and positive predictive value (40.2%, 29.3%, and 29.7%, respectively) with a P=.01, .03, .02, respectively. The ANN had the best receiver-operator area-under the curve (78.2%; P=.03). The BBN had the best precision-recall area-under the curve for detecting positive cases (30.4%; P=.03). Conclusions: Although delirium is inherently complex, preventive measures to mitigate its negative effect can be applied proactively if patients at risk are prospectively identified. Our results highlight 2 important points: (1) addressing class imbalance on the training dataset will augment machine learning model's performance in identifying patients likely to develop postoperative delirium, and (2) as the prediction of postoperative delirium is difficult because it is multifactorial and has complex pathophysiology, applying machine learning methods (complex or simple) may improve the prediction by revealing hidden patterns, which will lead to cost reduction by prevention of complications and will optimize patients' outcomes.},
keywords = {delirium; cardiac surgery; machine learning; predictive modeling},
pubstate = {published},
tppubtype = {article}
}
Background: Delirium is a temporary mental disorder that occasionally affects patients undergoing surgery, especially cardiac surgery. It is strongly associated with major adverse events, which in turn leads to increased cost and poor outcomes (eg, need for nursing home due to cognitive impairment, stroke, and death). The ability to foresee patients at risk of delirium will guide the timely initiation of multimodal preventive interventions, which will aid in reducing the burden and negative consequences associated with delirium. Several studies have focused on the prediction of delirium. However, the number of studies in cardiac surgical patients that have used machine learning methods is very limited. Objective: This study aimed to explore the application of several machine learning predictive models that can pre-emptively predict delirium in patients undergoing cardiac surgery and compare their performance. Methods: We investigated a number of machine learning methods to develop models that can predict delirium after cardiac surgery. A clinical dataset comprising over 5000 actual patients who underwent cardiac surgery in a single center was used to develop the models using logistic regression, artificial neural networks (ANN), support vector machines (SVM), Bayesian belief networks (BBN), na"ive Bayesian, random forest, and decision trees. Results: Only 507 out of 5584 patients (11.4%) developed delirium. We addressed the underlying class imbalance, using random undersampling, in the training dataset. The final prediction performance was validated on a separate test dataset. Owing to the target class imbalance, several measures were used to evaluate algorithm's performance for the delirium class on the test dataset. Out of the selected algorithms, the SVM algorithm had the best F1 score for positive cases, kappa, and positive predictive value (40.2%, 29.3%, and 29.7%, respectively) with a P=.01, .03, .02, respectively. The ANN had the best receiver-operator area-under the curve (78.2%; P=.03). The BBN had the best precision-recall area-under the curve for detecting positive cases (30.4%; P=.03). Conclusions: Although delirium is inherently complex, preventive measures to mitigate its negative effect can be applied proactively if patients at risk are prospectively identified. Our results highlight 2 important points: (1) addressing class imbalance on the training dataset will augment machine learning model's performance in identifying patients likely to develop postoperative delirium, and (2) as the prediction of postoperative delirium is difficult because it is multifactorial and has complex pathophysiology, applying machine learning methods (complex or simple) may improve the prediction by revealing hidden patterns, which will lead to cost reduction by prevention of complications and will optimize patients' outcomes.
Doerthe Arndt; William Van Woensel
Towards Supporting Multiple Semantics of Named Graphs Using N3 Rules Proceedings Article
In: Proceedings of the 13th RuleML+RR 2019 Doctoral Consortium and Rule Challenge, September 16-19, 2019 - Bolzano, Italy, September 16-24, 2019, CEUR-WS.org, 2019.
Abstract | Links | BibTeX | Tags:
@inproceedings{DBLP:conf/ruleml/ArndtW19,
title = {Towards Supporting Multiple Semantics of Named Graphs Using N3 Rules},
author = {Doerthe Arndt and William Van Woensel},
url = {http://ceur-ws.org/Vol-2438/paper6.pdf},
year = {2019},
date = {2019-01-01},
booktitle = {Proceedings of the 13th RuleML+RR 2019 Doctoral Consortium and Rule Challenge, September 16-19, 2019 - Bolzano, Italy, September 16-24, 2019},
volume = {2438},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
abstract = {Semantic Web applications often require the partitioning of triples into subgraphs, and then associating them with useful metadata(e.g., provenance). This led to the introduction of RDF datasets, with each RDF dataset comprising a default graph and zero or more named graphs. However, due to differences in RDF implementations, no consensus could be reached on a standard semantics; and a range of different dataset semantics are currently assumed. For an RDF system not be limited to only a subset of online RDF datasets, the system would need to be extended to support different dataset semantics—exactly the problem that eluded consensus before. In this paper, we transpose this problem to Notation3 Logic, an RDF-based rule language that similarly allows citing graphs within RDF documents. We propose a solution where an N3 author can directly indicate the intended semantics of a cited graph—possibly, combining multiple semantics within a single document. We supply an initial set of companion N3 rules, which implement a number of RDF dataset semantics, which allow an N3-compliant system to easily support multiple different semantics.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Semantic Web applications often require the partitioning of triples into subgraphs, and then associating them with useful metadata(e.g., provenance). This led to the introduction of RDF datasets, with each RDF dataset comprising a default graph and zero or more named graphs. However, due to differences in RDF implementations, no consensus could be reached on a standard semantics; and a range of different dataset semantics are currently assumed. For an RDF system not be limited to only a subset of online RDF datasets, the system would need to be extended to support different dataset semantics—exactly the problem that eluded consensus before. In this paper, we transpose this problem to Notation3 Logic, an RDF-based rule language that similarly allows citing graphs within RDF documents. We propose a solution where an N3 author can directly indicate the intended semantics of a cited graph—possibly, combining multiple semantics within a single document. We supply an initial set of companion N3 rules, which implement a number of RDF dataset semantics, which allow an N3-compliant system to easily support multiple different semantics.
2018
Syed S.R. Abidi; Patrice C. Roy; Muhammad S. Shah; Jin Yu; Sanjun Yan
A data mining framework for glaucoma decision support based on optic nerve image analysis using machine learning methods Journal Article
In: Journal of Healthcare Informatics Research, vol. 2, no. 4, pp. 370-401, 2018.
Links | BibTeX | Tags: Classification, Clustering, Data Mining, Decision Support Systems, Optic Nerve
@article{Abidi2018,
title = {A data mining framework for glaucoma decision support based on optic nerve image analysis using machine learning methods},
author = {Syed S.R. Abidi and Patrice C. Roy and Muhammad S. Shah and Jin Yu and Sanjun Yan},
doi = {10.1007/s41666-018-0028-7},
year = {2018},
date = {2018-12-01},
journal = {Journal of Healthcare Informatics Research},
volume = {2},
number = {4},
pages = {370-401},
keywords = {Classification, Clustering, Data Mining, Decision Support Systems, Optic Nerve},
pubstate = {published},
tppubtype = {article}
}
Tyler S Wheeler; Michael T Vallis; Nicholas B Giacomantonio; Samina R Abidi
Feasibility and usability of an ontology-based mobile intervention for patients with hypertension Journal Article
In: International Journal of Medical Informatics, vol. 119, pp. 8 - 16, 2018, ISSN: 1386-5056.
Links | BibTeX | Tags: Behaviour change, Chronic disease self-management, Hypertension, Mobile Health, Ontology
@article{WHEELER20188,
title = {Feasibility and usability of an ontology-based mobile intervention for patients with hypertension},
author = {Tyler S Wheeler and Michael T Vallis and Nicholas B Giacomantonio and Samina R Abidi},
url = {http://www.sciencedirect.com/science/article/pii/S1386505618301710},
doi = {10.1016/j.ijmedinf.2018.08.002},
issn = {1386-5056},
year = {2018},
date = {2018-11-01},
journal = {International Journal of Medical Informatics},
volume = {119},
pages = {8 - 16},
keywords = {Behaviour change, Chronic disease self-management, Hypertension, Mobile Health, Ontology},
pubstate = {published},
tppubtype = {article}
}
William Van Woensel; Syed Sibte Raza Abidi
Benchmarking Semantic Reasoning on Mobile Platforms: Towards Optimization Using OWL2 RL Journal Article
In: Semantic Web Journal, 2018.
Abstract | Links | BibTeX | Tags: Mobile Computing, OWL2 RL, Semantic Web reasoning
@article{SWJ-WVW-2018,
title = {Benchmarking Semantic Reasoning on Mobile Platforms: Towards Optimization Using OWL2 RL},
author = {William Van Woensel and Syed Sibte Raza Abidi},
url = {http://www.semantic-web-journal.net/system/files/swj1881.pdf},
year = {2018},
date = {2018-08-06},
journal = {Semantic Web Journal},
abstract = {Mobile hardware has advanced to a point where apps may consume the Semantic Web of Data, as exemplified in domains such as mobile context-awareness, m-Health, m-Tourism and augmented reality. However, recent work shows that the performance of ontology-based reasoning, an essential Semantic Web building block, still leaves much to be desired on mobile platforms. This presents a clear need to provide developers with the ability to benchmark mobile reasoning performance, based on their particular application scenarios, i.e., including reasoning tasks, process flows and datasets, to establish the feasibility of mobile deployment. In this regard, we present a mobile benchmark framework called MobiBench to help developers to benchmark semantic reasoners on mobile platforms. To realize efficient mobile, ontology-based reasoning, OWL2 RL is a promising solution since it (a) trades expressivity for scalability, which is important on resource-constrained platforms; and (b) provides unique opportunities for optimization due to its rule-based axiomatization. In this vein, we propose selections of OWL2 RL rule subsets for optimization purposes, based on several orthogonal dimensions. We extended MobiBench to support OWL2 RL and the proposed ruleset selections, and benchmarked multiple OWL2 RL-enabled rule engines and OWL reasoners on a mobile platform. Our results show significant performance improvements by applying OWL2 RL rule subsets, allowing performant reasoning for small datasets on mobile systems.},
keywords = {Mobile Computing, OWL2 RL, Semantic Web reasoning},
pubstate = {published},
tppubtype = {article}
}
Mobile hardware has advanced to a point where apps may consume the Semantic Web of Data, as exemplified in domains such as mobile context-awareness, m-Health, m-Tourism and augmented reality. However, recent work shows that the performance of ontology-based reasoning, an essential Semantic Web building block, still leaves much to be desired on mobile platforms. This presents a clear need to provide developers with the ability to benchmark mobile reasoning performance, based on their particular application scenarios, i.e., including reasoning tasks, process flows and datasets, to establish the feasibility of mobile deployment. In this regard, we present a mobile benchmark framework called MobiBench to help developers to benchmark semantic reasoners on mobile platforms. To realize efficient mobile, ontology-based reasoning, OWL2 RL is a promising solution since it (a) trades expressivity for scalability, which is important on resource-constrained platforms; and (b) provides unique opportunities for optimization due to its rule-based axiomatization. In this vein, we propose selections of OWL2 RL rule subsets for optimization purposes, based on several orthogonal dimensions. We extended MobiBench to support OWL2 RL and the proposed ruleset selections, and benchmarked multiple OWL2 RL-enabled rule engines and OWL reasoners on a mobile platform. Our results show significant performance improvements by applying OWL2 RL rule subsets, allowing performant reasoning for small datasets on mobile systems.
Hossein Mohammadhassanzadeh; Samina Abidi; William Van Woensel; Syed Sibte Raza Abidi
Investigating Plausible Reasoning over Knowledge Graphs for Semantics-based Health Data Analytics Proceedings Article
In: Data Exploration in the Web 3.0 Age (DEW) conference track at 27th IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE’18), IEEE, Paris, France, 2018.
Links | BibTeX | Tags: Plausible reasoning, Query Rewriting, Semantic Analytics, Semantic Web reasoning
@inproceedings{Mohammadhassanzadeh;2018,
title = {Investigating Plausible Reasoning over Knowledge Graphs for Semantics-based Health Data Analytics},
author = {Hossein Mohammadhassanzadeh and Samina Abidi and William Van Woensel and Syed Sibte Raza Abidi},
url = {https://ieeexplore.ieee.org/document/8495925},
year = {2018},
date = {2018-06-27},
booktitle = {Data Exploration in the Web 3.0 Age (DEW) conference track at 27th IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE’18)},
publisher = {IEEE},
address = {Paris, France},
keywords = {Plausible reasoning, Query Rewriting, Semantic Analytics, Semantic Web reasoning},
pubstate = {published},
tppubtype = {inproceedings}
}
William Van Woensel; Syed Sibte Raza Abidi
Optimizing Semantic Reasoning on Memory-Constrained Platforms using the RETE Algorithm Proceedings Article
In: 15th Extended Semantic Web Conference (ESWC 2018), pp. 682-696, Springer LNCS, Heraklion, Greece, 2018.
Abstract | Links | BibTeX | Tags: Mobile Computing, OWL2 RL, RETE, Semantic Web reasoning
@inproceedings{ESWC-WVW-2018,
title = {Optimizing Semantic Reasoning on Memory-Constrained Platforms using the RETE Algorithm},
author = {William Van Woensel and Syed Sibte Raza Abidi},
doi = {10.1007/978-3-319-93417-4_44},
year = {2018},
date = {2018-06-07},
booktitle = {15th Extended Semantic Web Conference (ESWC 2018)},
pages = {682-696},
publisher = {Springer LNCS},
address = {Heraklion, Greece},
abstract = {Mobile hardware improvements have opened the door for deploying rule systems on ubiquitous, mobile platforms. By executing rule-based tasks locally, less re-mote (cloud) resources are needed, bandwidth usage is reduced, and local, time-sensitive tasks are no longer influenced by network conditions. Further, with data being increasingly published in semantic format, an opportunity arises for rule systems to leverage the embedded semantics of semantic, ontology-based data. To support this kind of ontology-based reasoning in rule systems, rule-based axiomatizations of ontology semantics can be utilized (e.g., OWL 2 RL). Nonetheless, recent benchmarks have found that any kind of ontology-based reasoning on mobile platforms still lacks scalability, at least when directly re-using existing (PC- or server-based) technologies. To create a tailored solution for resource-constrained platforms, we propose changes to RETE, the mainstay algorithm for production rule systems. In particular, we present an adapted algorithm that, by selectively pooling RETE memories, aims to better balance memory usage with performance. Further, we show that this algorithm is well-suited towards many typical Semantic Web scenarios. Using our custom algorithm, we perform an extensive evaluation of semantic reasoning both on the PC and mobile platform.},
keywords = {Mobile Computing, OWL2 RL, RETE, Semantic Web reasoning},
pubstate = {published},
tppubtype = {inproceedings}
}
Mobile hardware improvements have opened the door for deploying rule systems on ubiquitous, mobile platforms. By executing rule-based tasks locally, less re-mote (cloud) resources are needed, bandwidth usage is reduced, and local, time-sensitive tasks are no longer influenced by network conditions. Further, with data being increasingly published in semantic format, an opportunity arises for rule systems to leverage the embedded semantics of semantic, ontology-based data. To support this kind of ontology-based reasoning in rule systems, rule-based axiomatizations of ontology semantics can be utilized (e.g., OWL 2 RL). Nonetheless, recent benchmarks have found that any kind of ontology-based reasoning on mobile platforms still lacks scalability, at least when directly re-using existing (PC- or server-based) technologies. To create a tailored solution for resource-constrained platforms, we propose changes to RETE, the mainstay algorithm for production rule systems. In particular, we present an adapted algorithm that, by selectively pooling RETE memories, aims to better balance memory usage with performance. Further, we show that this algorithm is well-suited towards many typical Semantic Web scenarios. Using our custom algorithm, we perform an extensive evaluation of semantic reasoning both on the PC and mobile platform.
Raquel Dias; K. Guimarães; M. Lima; JG. Alves; Syed Sibte Raza Abidi
A Mobile Early Stimulation Program to Support Children With Developmental Delays in Brazil Proceedings Article
In: 28th European Medical Informatics Conference (MIE2018), April 24-26, 2018, IOSPress, Gothenburg, Sweden, 2018.
Abstract | Links | BibTeX | Tags: Clinical Decision Support Systems, Health Informatics, Patient Education
@inproceedings{dias-04-18,
title = {A Mobile Early Stimulation Program to Support Children With Developmental Delays in Brazil},
author = {Raquel Dias and K. Guimarães and M. Lima and JG. Alves and Syed Sibte Raza Abidi},
url = {https://www.ncbi.nlm.nih.gov/pubmed/29678068},
year = {2018},
date = {2018-04-24},
booktitle = {28th European Medical Informatics Conference (MIE2018), April 24-26, 2018},
publisher = {IOSPress},
address = {Gothenburg, Sweden},
abstract = {Developmental delay is a deviation development from the normative milestones during the childhood and it may be caused by neurological disorders. Early stimulation is a standardized and simple technique to treat developmental delays in children (aged 0-3 years), allowing them to reach the best development possible and to mitigate neuropsychomotor sequelae. However, the outcomes of the treatment depending on the involvement of the family, to continue the activities at home on a daily basis. To empower and educate parents of children with neurodevelopmental delays to administer standardized early stimulation programs at home, we developed a mobile early stimulation program that provides timely and evidence-based clinical decision support to health professionals and a personalized guidance to parents about how to administer early stimulation to their child at home.},
keywords = {Clinical Decision Support Systems, Health Informatics, Patient Education},
pubstate = {published},
tppubtype = {inproceedings}
}
Developmental delay is a deviation development from the normative milestones during the childhood and it may be caused by neurological disorders. Early stimulation is a standardized and simple technique to treat developmental delays in children (aged 0-3 years), allowing them to reach the best development possible and to mitigate neuropsychomotor sequelae. However, the outcomes of the treatment depending on the involvement of the family, to continue the activities at home on a daily basis. To empower and educate parents of children with neurodevelopmental delays to administer standardized early stimulation programs at home, we developed a mobile early stimulation program that provides timely and evidence-based clinical decision support to health professionals and a personalized guidance to parents about how to administer early stimulation to their child at home.
Ben Rose Davis; E. Stringer; Samina Abidi; Syed Sibte Raza Abidi
Interactive Dialogue-Based Patient Education for Juvenile Idiopathic Arthritis Using Argument Theory Proceedings Article
In: 28th European Medical Informatics Conference (MIE2018), April 24 - 26, IOSPress, Gothenburg, Sweden, 2018.
Abstract | Links | BibTeX | Tags: Clinical Decision Support Systems, Health Informatics, Patient Education, Personalized Medicine
@inproceedings{davis-mie-2018,
title = {Interactive Dialogue-Based Patient Education for Juvenile Idiopathic Arthritis Using Argument Theory},
author = {Ben Rose Davis and E. Stringer and Samina Abidi and Syed Sibte Raza Abidi},
url = {https://www.ncbi.nlm.nih.gov/pubmed/29678020},
year = {2018},
date = {2018-04-24},
booktitle = {28th European Medical Informatics Conference (MIE2018), April 24 - 26},
publisher = {IOSPress},
address = {Gothenburg, Sweden},
abstract = {Families of children with Juvenile Idiopathic Arthritis need a way to interact with Patient Education Materials (PEM) so that learning occurs at their own pace, on topics that are relevant to them. This paper proposes a novel, dialogue-based approach to address these needs. Using an extended version of Toulmin's model of argument as a theory-based classification method, we digitized paper-based PEM to render an interactive dialogue. The dialogue allows the user to explore a topic with respect to their interests and apprehensions as opposed to providing a static, generic document.},
keywords = {Clinical Decision Support Systems, Health Informatics, Patient Education, Personalized Medicine},
pubstate = {published},
tppubtype = {inproceedings}
}
Families of children with Juvenile Idiopathic Arthritis need a way to interact with Patient Education Materials (PEM) so that learning occurs at their own pace, on topics that are relevant to them. This paper proposes a novel, dialogue-based approach to address these needs. Using an extended version of Toulmin's model of argument as a theory-based classification method, we digitized paper-based PEM to render an interactive dialogue. The dialogue allows the user to explore a topic with respect to their interests and apprehensions as opposed to providing a static, generic document.
Ali Daowd; Samina Abidi; Ashraf Abusharekh; Syed Sibte Raza Abidi
A Personalized Risk Stratification Platform for Population Lifetime Healthcare Proceedings Article
In: 28th European Medical Informatics Conference (MIE2018), April 24 - 26, IOSPress, Gothenburg, Sweden, 2018.
Abstract | Links | BibTeX | Tags: Clinical Decision Support Systems, Health Informatics, Personalized Medicine
@inproceedings{daowd-mie-2018,
title = {A Personalized Risk Stratification Platform for Population Lifetime Healthcare},
author = {Ali Daowd and Samina Abidi and Ashraf Abusharekh and Syed Sibte Raza Abidi},
url = {https://www.ncbi.nlm.nih.gov/pubmed/29678095},
year = {2018},
date = {2018-04-24},
booktitle = {28th European Medical Informatics Conference (MIE2018), April 24 - 26},
publisher = {IOSPress},
address = {Gothenburg, Sweden},
abstract = {Chronic diseases are the leading cause of death worldwide. It is well understood that if modifiable risk factors are targeted, most chronic diseases can be prevented. Lifetime health is an emerging health paradigm that aims to assist individuals to achieve desired health targets, and avoid harmful lifecycle choices to mitigate the risk of chronic diseases. Early risk identification is central to lifetime health. In this paper, we present a digital health-based platform (PRISM) that leverages artificial intelligence, data visualization and mobile health technologies to empower citizens to self-assess, self-monitor and self-manage their overall risk of major chronic diseases and pursue personalized chronic disease prevention programs. PRISM offers risk assessment tools for 5 chronic conditions, 2 psychiatric disorders and 8 different cancers.},
keywords = {Clinical Decision Support Systems, Health Informatics, Personalized Medicine},
pubstate = {published},
tppubtype = {inproceedings}
}
Chronic diseases are the leading cause of death worldwide. It is well understood that if modifiable risk factors are targeted, most chronic diseases can be prevented. Lifetime health is an emerging health paradigm that aims to assist individuals to achieve desired health targets, and avoid harmful lifecycle choices to mitigate the risk of chronic diseases. Early risk identification is central to lifetime health. In this paper, we present a digital health-based platform (PRISM) that leverages artificial intelligence, data visualization and mobile health technologies to empower citizens to self-assess, self-monitor and self-manage their overall risk of major chronic diseases and pursue personalized chronic disease prevention programs. PRISM offers risk assessment tools for 5 chronic conditions, 2 psychiatric disorders and 8 different cancers.
Samina Abidi; Michael Vallis; Helena Piccinini-Vallis; Ali Syed Imran; Raza Syed Sibte Abidi
In: JMIR Med Inform, vol. 6, no. 2, pp. e25, 2018.
Abstract | Links | BibTeX | Tags: clinical decision support system
@article{info:doi/10.2196/medinform.9629c,
title = {Diabetes-Related Behavior Change Knowledge Transfer to Primary Care Practitioners and Patients: Implementation and Evaluation of a Digital Health Platform},
author = {Samina Abidi and Michael Vallis and Helena Piccinini-Vallis and Ali Syed Imran and Raza Syed Sibte Abidi},
url = {http://medinform.jmir.org/2018/2/e25/},
doi = {10.2196/medinform.9629},
year = {2018},
date = {2018-04-18},
journal = {JMIR Med Inform},
volume = {6},
number = {2},
pages = {e25},
abstract = {Background: Behavioral science is now being integrated into diabetes self-management interventions. However, the challenge that presents itself is how to translate these knowledge resources during care so that primary care practitioners can use them to offer evidence-informed behavior change support and diabetes management recommendations to patients with diabetes. Objective: The aim of this study was to develop and evaluate a computerized decision support platform called ``Diabetes Web-Centric Information and Support Environment'' (DWISE) that assists primary care practitioners in applying standardized behavior change strategies and clinical practice guidelines--based recommendations to an individual patient and empower the patient with the skills and knowledge required to self-manage their diabetes through planned, personalized, and pervasive behavior change strategies. Methods: A health care knowledge management approach is used to implement DWISE so that it features the following functionalities: (1) assessment of primary care practitioners' readiness to administer validated behavior change interventions to patients with diabetes; (2) educational support for primary care practitioners to help them offer behavior change interventions to patients; (3) access to evidence-based material, such as the Canadian Diabetes Association's (CDA) clinical practice guidelines, to primary care practitioners; (4) development of personalized patient self-management programs to help patients with diabetes achieve healthy behaviors to meet CDA targets for managing type 2 diabetes; (5) educational support for patients to help them achieve behavior change; and (6) monitoring of the patients' progress to assess their adherence to the behavior change program and motivating them to ensure compliance with their program. DWISE offers these functionalities through an interactive Web-based interface to primary care practitioners, whereas the patient's self-management program and associated behavior interventions are delivered through a mobile patient diary via mobile phones and tablets. DWISE has been tested for its usability, functionality, usefulness, and acceptance through a series of qualitative studies. Results: For the primary care practitioner tool, most usability problems were associated with the navigation of the tool and the presentation, formatting, understandability, and suitability of the content. For the patient tool, most issues were related to the tool's screen layout, design features, understandability of the content, clarity of the labels used, and navigation across the tool. Facilitators and barriers to DWISE use in a shared decision-making environment have also been identified. Conclusions: This work has provided a unique electronic health solution to translate complex health care knowledge in terms of easy-to-use, evidence-informed, point-of-care decision aids for primary care practitioners. Patients' feedback is now being used to make necessary modification to DWISE.},
keywords = {clinical decision support system},
pubstate = {published},
tppubtype = {article}
}
Background: Behavioral science is now being integrated into diabetes self-management interventions. However, the challenge that presents itself is how to translate these knowledge resources during care so that primary care practitioners can use them to offer evidence-informed behavior change support and diabetes management recommendations to patients with diabetes. Objective: The aim of this study was to develop and evaluate a computerized decision support platform called ``Diabetes Web-Centric Information and Support Environment'' (DWISE) that assists primary care practitioners in applying standardized behavior change strategies and clinical practice guidelines--based recommendations to an individual patient and empower the patient with the skills and knowledge required to self-manage their diabetes through planned, personalized, and pervasive behavior change strategies. Methods: A health care knowledge management approach is used to implement DWISE so that it features the following functionalities: (1) assessment of primary care practitioners' readiness to administer validated behavior change interventions to patients with diabetes; (2) educational support for primary care practitioners to help them offer behavior change interventions to patients; (3) access to evidence-based material, such as the Canadian Diabetes Association's (CDA) clinical practice guidelines, to primary care practitioners; (4) development of personalized patient self-management programs to help patients with diabetes achieve healthy behaviors to meet CDA targets for managing type 2 diabetes; (5) educational support for patients to help them achieve behavior change; and (6) monitoring of the patients' progress to assess their adherence to the behavior change program and motivating them to ensure compliance with their program. DWISE offers these functionalities through an interactive Web-based interface to primary care practitioners, whereas the patient's self-management program and associated behavior interventions are delivered through a mobile patient diary via mobile phones and tablets. DWISE has been tested for its usability, functionality, usefulness, and acceptance through a series of qualitative studies. Results: For the primary care practitioner tool, most usability problems were associated with the navigation of the tool and the presentation, formatting, understandability, and suitability of the content. For the patient tool, most issues were related to the tool's screen layout, design features, understandability of the content, clarity of the labels used, and navigation across the tool. Facilitators and barriers to DWISE use in a shared decision-making environment have also been identified. Conclusions: This work has provided a unique electronic health solution to translate complex health care knowledge in terms of easy-to-use, evidence-informed, point-of-care decision aids for primary care practitioners. Patients' feedback is now being used to make necessary modification to DWISE.
Jafna L Cox; Ratika Parkash; Syed SR Abidi; Lehana Thabane; Feng Xie; James MacKillop; Samina R Abidi; Antonio Ciaccia; Shurjeel H Choudhri; A Abusharekh; Joanna Nemis-White
In: vol. 201, pp. 149 - 157, 2018, ISSN: 0002-8703.
@article{COX2018149,
author = {Jafna L Cox and Ratika Parkash and Syed SR Abidi and Lehana Thabane and Feng Xie and James MacKillop and Samina R Abidi and Antonio Ciaccia and Shurjeel H Choudhri and A Abusharekh and Joanna Nemis-White},
url = {http://www.sciencedirect.com/science/article/pii/S0002870318301170},
doi = {https://doi.org/10.1016/j.ahj.2018.04.008},
issn = {0002-8703},
year = {2018},
date = {2018-01-01},
volume = {201},
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Jafna L Cox; Ratika Parkash; Syed SR Abidi; Lehana Thabane; Feng Xie; James MacKillop; Samina R Abidi; Antonio Ciaccia; Shurjeel H Choudhri; A Abusharekh; Joanna Nemis-White
In: vol. 201, pp. 149 - 157, 2018, ISSN: 0002-8703.
@article{COX2018149b,
author = {Jafna L Cox and Ratika Parkash and Syed SR Abidi and Lehana Thabane and Feng Xie and James MacKillop and Samina R Abidi and Antonio Ciaccia and Shurjeel H Choudhri and A Abusharekh and Joanna Nemis-White},
url = {http://www.sciencedirect.com/science/article/pii/S0002870318301170},
doi = {https://doi.org/10.1016/j.ahj.2018.04.008},
issn = {0002-8703},
year = {2018},
date = {2018-01-01},
volume = {201},
pages = {149 - 157},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Jafna L Cox; Ratika Parkash; Syed SR Abidi; Lehana Thabane; Feng Xie; James MacKillop; Samina R Abidi; Antonio Ciaccia; Shurjeel H Choudhri; A Abusharekh; Joanna Nemis-White
In: vol. 201, pp. 149 - 157, 2018, ISSN: 0002-8703.
@article{COX2018149c,
title = {Optimizing Primary Care Management of Atrial Fibrillation: The Rationale and Methods of the Integrated Management Program Advancing Community Treatment of Atrial Fibrillation (IMPACT-AF) Study},
author = {Jafna L Cox and Ratika Parkash and Syed SR Abidi and Lehana Thabane and Feng Xie and James MacKillop and Samina R Abidi and Antonio Ciaccia and Shurjeel H Choudhri and A Abusharekh and Joanna Nemis-White},
url = {http://www.sciencedirect.com/science/article/pii/S0002870318301170},
doi = {10.1016/j.ahj.2018.04.008},
issn = {0002-8703},
year = {2018},
date = {2018-01-01},
volume = {201},
pages = {149 - 157},
keywords = {},
pubstate = {published},
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}
Ali Naserian Mojadam; Najaf Nadeem; Hussein Beydoun; Samina Abidi; Ali Rizvi; Syed Abidi
Preoperative Education System to Assist Patients Undergoing TAVI Surgery: A Digital Health Solution Journal Article
In: Journal of Health & Medical Informatics, vol. 09, 2018.
@article{articleb,
title = {Preoperative Education System to Assist Patients Undergoing TAVI Surgery: A Digital Health Solution},
author = {Ali Naserian Mojadam and Najaf Nadeem and Hussein Beydoun and Samina Abidi and Ali Rizvi and Syed Abidi},
doi = {10.4172/2157-7420.1000313},
year = {2018},
date = {2018-01-01},
journal = {Journal of Health & Medical Informatics},
volume = {09},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2017
William Van Woensel; Wasif Baig; Syed Sibte Raza Abidi; Samina Abidi
A Semantic Web Framework for Behavioral User Modeling and Action Planning for Personalized Behavior Modification Proceedings Article
In: 10th International Conference on Semantic Web Applications and Tools for Life Sciences, CEUR, Rome, Italy, 2017.
Links | BibTeX | Tags: Behaviour Modelling, Behavioural Change Theory, Personalized Medicine
@inproceedings{SCT2017,
title = {A Semantic Web Framework for Behavioral User Modeling and Action Planning for Personalized Behavior Modification},
author = {William Van Woensel and Wasif Baig and Syed Sibte Raza Abidi and Samina Abidi},
url = {https://niche.cs.dal.ca/wp-content/uploads/2017/12/paper-21-camera-ready-1.pdf},
year = {2017},
date = {2017-12-06},
booktitle = {10th International Conference on Semantic Web Applications and Tools for Life Sciences},
publisher = {CEUR},
address = {Rome, Italy},
keywords = {Behaviour Modelling, Behavioural Change Theory, Personalized Medicine},
pubstate = {published},
tppubtype = {inproceedings}
}
William Van Woensel; Patrice C. Roy; Syed Sibte Raza Abidi
Achieving Pro-Active Guidance of Patients through ADL via Knowledge-Driven Activity Recognition and Complex Semantic Workflows Proceedings Article
In: 10th International Conference on Semantic Web Applications and Tools for Life Sciences, CEUR, Rome, Italy, 2017.
Links | BibTeX | Tags: Activity Recognition, Ambient Intelligence, Semantic Web reasoning
@inproceedings{ADL201,
title = {Achieving Pro-Active Guidance of Patients through ADL via Knowledge-Driven Activity Recognition and Complex Semantic Workflows},
author = {William Van Woensel and Patrice C. Roy and Syed Sibte Raza Abidi},
url = {https://niche.cs.dal.ca/wp-content/uploads/2017/12/paper_camera-ready.pdf},
year = {2017},
date = {2017-12-06},
booktitle = {10th International Conference on Semantic Web Applications and Tools for Life Sciences},
publisher = {CEUR},
address = {Rome, Italy},
keywords = {Activity Recognition, Ambient Intelligence, Semantic Web reasoning},
pubstate = {published},
tppubtype = {inproceedings}
}
Hossein Mohammadhassanzadeh; Samina Raza Abidi; Mohammad Salman Shah; Mehdi Karamollahi; Syed Sibte Raza Abidi
SeDAn: A Plausible Reasoning Approach for Semantics-based Data Analytics in Healthcare Proceedings Article
In: Workshop on Artificial Intelligence with Application in Health, 16th International Conference of the Italian Association for Artificial Intelligence, Bari, Italy, 2017.
Links | BibTeX | Tags: Health Data Analytics, Knowledge Management, Plausible reasoning, Semantic Web
@inproceedings{DBLP:conf/aiia/Mohammadhassanzadeh17,
title = {SeDAn: A Plausible Reasoning Approach for Semantics-based Data Analytics in Healthcare},
author = {Hossein Mohammadhassanzadeh and Samina Raza Abidi and Mohammad Salman Shah and Mehdi Karamollahi and Syed Sibte Raza Abidi},
url = {http://ceur-ws.org/Vol-1982/paper7.pdf},
year = {2017},
date = {2017-11-14},
booktitle = {Workshop on Artificial Intelligence with Application in Health, 16th International Conference of the Italian Association for Artificial Intelligence},
address = {Bari, Italy},
keywords = {Health Data Analytics, Knowledge Management, Plausible reasoning, Semantic Web},
pubstate = {published},
tppubtype = {inproceedings}
}
Samina Abidi
In: J. Medical Systems, vol. 41, no. 12, pp. 193:1 - 193:19, 2017.
Links | BibTeX | Tags: Clinical Decision Support Systems, Clinical Practice Guidelines, Comorbidities, Knowledge Modelling, Knowledge Translation
@article{DBLP:journals/jms/Abidi17,
title = {A Knowledge-Modeling Approach to Integrate Multiple Clinical Practice Guidelines to Provide Evidence-Based Clinical Decision Support for Managing Comorbid Conditions},
author = {Samina Abidi},
url = {https://doi.org/10.1007/s10916-017-0841-1},
doi = {10.1007/s10916-017-0841-1},
year = {2017},
date = {2017-10-16},
journal = {J. Medical Systems},
volume = {41},
number = {12},
pages = {193:1 - 193:19},
keywords = {Clinical Decision Support Systems, Clinical Practice Guidelines, Comorbidities, Knowledge Modelling, Knowledge Translation},
pubstate = {published},
tppubtype = {article}
}
Ehsan Maghsoud-Lou; Sean Christie; Samina Raza Abidi; Syed Sibte Raza Abidi
In: Journal of Medical Systems, vol. 41, no. 9, pp. 139, 2017, ISSN: 1573-689X.
Abstract | Links | BibTeX | Tags:
@article{Maghsoud-Lou2017,
title = {Protocol-Driven Decision Support within e-Referral Systems to Streamline Patient Consultation, Triaging and Referrals from Primary Care to Specialist Clinics},
author = {Ehsan Maghsoud-Lou and Sean Christie and Samina Raza Abidi and Syed Sibte Raza Abidi},
url = {https://doi.org/10.1007/s10916-017-0791-7},
doi = {10.1007/s10916-017-0791-7},
issn = {1573-689X},
year = {2017},
date = {2017-08-01},
journal = {Journal of Medical Systems},
volume = {41},
number = {9},
pages = {139},
abstract = {Patient referral is a protocol where the referring primary care physician refers the patient to a specialist for further treatment. The paper-based current referral process at times lead to communication and operational issues, resulting in either an unfulfilled referral request or an unnecessary referral request. Despite the availability of standardized referral protocols they are not readily applied because they are tedious and time-consuming, thus resulting in suboptimal referral requests. We present a semantic-web based Referral Knowledge Modeling and Execution Framework to computerize referral protocols, clinical guidelines and assessment tools in order to develop a computerized e-Referral system that offers protocol-based decision support to streamline and standardize the referral process. We have developed a Spinal Problem E-Referral (SPER) system that computerizes the Spinal Condition Consultation Protocol (SCCP) mandated by the Halifax Infirmary Division of Neurosurgery (Halifax, Canada) for referrals for spine related conditions (such as back pain). The SPER system executes the ontologically modeled SCCP to determine (i) patient's triaging option as per severity assessments stipulated by SCCP; and (b) clinical recommendations as per the clinical guidelines incorporated within SCCP. In operation, the SPER system identifies the critical cases and triages them for specialist referral, whereas for non-critical cases SPER system provides clinical guideline based recommendations to help the primary care physician effectively manage the patient. The SPER system has undergone a pilot usability study and was deemed to be easy to use by physicians with potential to improve the referral process within the Division of Neurosurgery at QEII Health Science Center, Halifax, Canada.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Patient referral is a protocol where the referring primary care physician refers the patient to a specialist for further treatment. The paper-based current referral process at times lead to communication and operational issues, resulting in either an unfulfilled referral request or an unnecessary referral request. Despite the availability of standardized referral protocols they are not readily applied because they are tedious and time-consuming, thus resulting in suboptimal referral requests. We present a semantic-web based Referral Knowledge Modeling and Execution Framework to computerize referral protocols, clinical guidelines and assessment tools in order to develop a computerized e-Referral system that offers protocol-based decision support to streamline and standardize the referral process. We have developed a Spinal Problem E-Referral (SPER) system that computerizes the Spinal Condition Consultation Protocol (SCCP) mandated by the Halifax Infirmary Division of Neurosurgery (Halifax, Canada) for referrals for spine related conditions (such as back pain). The SPER system executes the ontologically modeled SCCP to determine (i) patient's triaging option as per severity assessments stipulated by SCCP; and (b) clinical recommendations as per the clinical guidelines incorporated within SCCP. In operation, the SPER system identifies the critical cases and triages them for specialist referral, whereas for non-critical cases SPER system provides clinical guideline based recommendations to help the primary care physician effectively manage the patient. The SPER system has undergone a pilot usability study and was deemed to be easy to use by physicians with potential to improve the referral process within the Division of Neurosurgery at QEII Health Science Center, Halifax, Canada.
Patrice C. Roy; Samina R. Abidi; Syed S.R. Abidi
Possibilistic Activity Recognition with Uncertain Observations to Support Medication Adherence in an Assisted Ambient Living Setting Journal Article
In: Knowledge-Based Systems, vol. 133, pp. 156-173, 2017, ISSN: 09507051.
Abstract | Links | BibTeX | Tags: Activity Recognition, Ambient Intelligence, medication adherence
@article{Roy2017b,
title = {Possibilistic Activity Recognition with Uncertain Observations to Support Medication Adherence in an Assisted Ambient Living Setting},
author = {Patrice C. Roy and Samina R. Abidi and Syed S.R. Abidi},
url = {http://www.sciencedirect.com/science/article/pii/S0950705117303246},
doi = {10.1016/j.knosys.2017.07.008},
issn = {09507051},
year = {2017},
date = {2017-07-06},
journal = {Knowledge-Based Systems},
volume = {133},
pages = {156-173},
abstract = {A recent trend in healthcare is to motivate patients to self-manage their health conditions in home-based settings. Self-management programs guide and motivate patients to achieve self-efficacy in the self-management of their disease through a regime of educational and behavioural modification strategies. To improve self-management programs effectiveness and efficacy, we must consider Ambient Assisted Living (AAL) technologies (smart environments, activity recognition, aid acts planning), since they alleviate issues related to unreliable self-reported data by monitoring self-management activities. To improve self-management programs in smart environments, it is necessary to recognize the occupant behaviour from observed data. Observed data/attributes generated from various sources (sensors, questionnaires, low-level activity recognition) are certain to uncertain (imprecise, incomplete, missing), where several values are plausible instead of only one. Thus, activity recognition must consider heterogeneous observations (sources' types) and uncertainty in the activity recognition inputs (observations). To address this challenge, we propose an activity recognition approach based on possibilistic network classifiers with uncertain observations. We believe that this is the first work to consider possibilistic network classifiers for the recognition of activities in smart environments using uncertain observations. We have validated the approach on 780 synthetic scenarios illustrating behaviours related to medication adherence. The activity classifiers, based on knowledge and beliefs about the activities related to medication adherence, can correctly recognize 79% of an activity current state, which is comparable with approaches based on data-driven naïve Bayesian classifiers. Furthermore, the classification performance only decreases when we have highly partial to complete ignorance about the observations values. Hence, the validations results show the interest of activity recognition based on possibilistic network classifiers for handling uncertain observations.},
keywords = {Activity Recognition, Ambient Intelligence, medication adherence},
pubstate = {published},
tppubtype = {article}
}
A recent trend in healthcare is to motivate patients to self-manage their health conditions in home-based settings. Self-management programs guide and motivate patients to achieve self-efficacy in the self-management of their disease through a regime of educational and behavioural modification strategies. To improve self-management programs effectiveness and efficacy, we must consider Ambient Assisted Living (AAL) technologies (smart environments, activity recognition, aid acts planning), since they alleviate issues related to unreliable self-reported data by monitoring self-management activities. To improve self-management programs in smart environments, it is necessary to recognize the occupant behaviour from observed data. Observed data/attributes generated from various sources (sensors, questionnaires, low-level activity recognition) are certain to uncertain (imprecise, incomplete, missing), where several values are plausible instead of only one. Thus, activity recognition must consider heterogeneous observations (sources' types) and uncertainty in the activity recognition inputs (observations). To address this challenge, we propose an activity recognition approach based on possibilistic network classifiers with uncertain observations. We believe that this is the first work to consider possibilistic network classifiers for the recognition of activities in smart environments using uncertain observations. We have validated the approach on 780 synthetic scenarios illustrating behaviours related to medication adherence. The activity classifiers, based on knowledge and beliefs about the activities related to medication adherence, can correctly recognize 79% of an activity current state, which is comparable with approaches based on data-driven naïve Bayesian classifiers. Furthermore, the classification performance only decreases when we have highly partial to complete ignorance about the observations values. Hence, the validations results show the interest of activity recognition based on possibilistic network classifiers for handling uncertain observations.
Samina Abidi; Michael Vallis; Helena Piccinini-Vallis; Syed Ali Imran; Syed Sibte Raza Abidi
A Digital Framework to Support Providers and Patients in Diabetes Related Behavior Modification Proceedings Article
In: Informatics for Health (MIE2017), Manchester, April 24 - April 26, 2017, IOS Press, 2017.
BibTeX | Tags: Behaviour Modelling, Behavioural Change Theory, Diabetes Management
@inproceedings{Abidi2017,
title = {A Digital Framework to Support Providers and Patients in Diabetes Related Behavior Modification},
author = {Samina Abidi and Michael Vallis and Helena Piccinini-Vallis and Syed Ali Imran and Syed Sibte Raza Abidi},
year = {2017},
date = {2017-04-26},
booktitle = {Informatics for Health (MIE2017), Manchester, April 24 - April 26, 2017},
publisher = {IOS Press},
keywords = {Behaviour Modelling, Behavioural Change Theory, Diabetes Management},
pubstate = {published},
tppubtype = {inproceedings}
}
Samuel Alan Stewart; Syed Sibte Raza Abidi
Leveraging Medical Taxonomies to Improve Knowledge Management within Online Communities of Practice: The Knowledge Maps System Journal Article
In: Journal of Computer Methods and Programs in Biomedicine, vol. 143, pp. 121-127, 2017.
BibTeX | Tags: Knowledge Management, Online Practice Communities
@article{Stewart2017,
title = {Leveraging Medical Taxonomies to Improve Knowledge Management within Online Communities of Practice: The Knowledge Maps System},
author = {Samuel Alan Stewart and Syed Sibte Raza Abidi},
year = {2017},
date = {2017-04-26},
journal = {Journal of Computer Methods and Programs in Biomedicine},
volume = {143},
pages = {121-127},
keywords = {Knowledge Management, Online Practice Communities},
pubstate = {published},
tppubtype = {article}
}
Enayat Rajabi; Syed Sibte Raza Abidi
Discovering Central Practitioners in a Medical Discussion Forum Using Semantic Web Analytics Proceedings Article
In: Informatics for Health (MIE2017), Manchester, UK, pp. 486-490, European Federation for Medical Informatics (EFMI) and IOS Press, 2017, ISSN: 1879-8365.
Abstract | Links | BibTeX | Tags: Semantic Web, Semantic Web Analytics, Social Network Analysis
@inproceedings{Rajabi2017,
title = {Discovering Central Practitioners in a Medical Discussion Forum Using Semantic Web Analytics },
author = {Enayat Rajabi and Syed Sibte Raza Abidi },
doi = {10.3233/978-1-61499-753-5-486},
issn = {1879-8365},
year = {2017},
date = {2017-04-24},
booktitle = {Informatics for Health (MIE2017), Manchester, UK},
pages = {486-490},
publisher = {European Federation for Medical Informatics (EFMI) and IOS Press},
abstract = {The aim of this paper is to investigate semantic web based methods to
enrich and transform a medical discussion forum in order to perform semanticsdriven
social network analysis. We use the centrality measures as well as semantic
similarity metrics to identify the most influential practitioners within a discussion
forum. The centrality results of our approach are in line with centrality measures
produced by traditional SNA methods, thus validating the applicability of semantic
web based methods for SNA, particularly for analyzing social networks for
specialized discussion forums. },
keywords = {Semantic Web, Semantic Web Analytics, Social Network Analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
The aim of this paper is to investigate semantic web based methods to
enrich and transform a medical discussion forum in order to perform semanticsdriven
social network analysis. We use the centrality measures as well as semantic
similarity metrics to identify the most influential practitioners within a discussion
forum. The centrality results of our approach are in line with centrality measures
produced by traditional SNA methods, thus validating the applicability of semantic
web based methods for SNA, particularly for analyzing social networks for
specialized discussion forums.
enrich and transform a medical discussion forum in order to perform semanticsdriven
social network analysis. We use the centrality measures as well as semantic
similarity metrics to identify the most influential practitioners within a discussion
forum. The centrality results of our approach are in line with centrality measures
produced by traditional SNA methods, thus validating the applicability of semantic
web based methods for SNA, particularly for analyzing social networks for
specialized discussion forums.
Patrice C. Roy; Samina Raza Abidi; Syed Sibte Raza Abidi
Monitoring Activities Related to Medication Adherence in Ambient Assisted Living Environments Proceedings Article
In: Randell, Rebecca; Cornet, Ronald; McCowan, Colin; Peek, Niels; Scott, Philip J. (Ed.): Informatics for Health: Connected Citizen-Led Wellness and Population Health (MIE 2017), Manchester, UK, April 24th-26th 2017, pp. 28-32, European Federation for Medical Informatics (EFMI) and IOS Press, 2017, ISSN: 1879-8365.
Abstract | Links | BibTeX | Tags: Activity Recognition, Ambient Intelligence, medication adherence
@inproceedings{Roy2017,
title = {Monitoring Activities Related to Medication Adherence in Ambient Assisted Living Environments},
author = {Patrice C. Roy and Samina Raza Abidi and Syed Sibte Raza Abidi},
editor = {Rebecca Randell and Ronald Cornet and Colin McCowan and Niels Peek and Philip J. Scott},
doi = {10.3233/978-1-61499-753-5-28},
issn = {1879-8365},
year = {2017},
date = {2017-04-24},
booktitle = {Informatics for Health: Connected Citizen-Led Wellness and Population Health (MIE 2017), Manchester, UK, April 24th-26th 2017},
volume = {235},
pages = {28-32},
publisher = {European Federation for Medical Informatics (EFMI) and IOS Press},
series = {Studies in Health Technology and Informatics},
abstract = {A recent trend in healthcare is to motivate patients to self-manage their health conditions in home-based settings. Medication adherence is an important aspect in disease self-management since sub-optimal medication adherence by the patient can lead to serious healthcare costs and discomfort for the patient. In order to alleviate the limitations of self-reported medication adherence, we can use ambient assistive living (AAL) technologies in smart environments. Activity recognition services allow to retrieve self-management information related to medication adherence in a less intrusive way. By remotely monitor compliance with medication adherence, self-management program’s interventions can be tailored and adapted based on the observed patient’s behaviour. To address this challenge, we present an AAL framework that monitor activities related to medication adherence.},
keywords = {Activity Recognition, Ambient Intelligence, medication adherence},
pubstate = {published},
tppubtype = {inproceedings}
}
A recent trend in healthcare is to motivate patients to self-manage their health conditions in home-based settings. Medication adherence is an important aspect in disease self-management since sub-optimal medication adherence by the patient can lead to serious healthcare costs and discomfort for the patient. In order to alleviate the limitations of self-reported medication adherence, we can use ambient assistive living (AAL) technologies in smart environments. Activity recognition services allow to retrieve self-management information related to medication adherence in a less intrusive way. By remotely monitor compliance with medication adherence, self-management program’s interventions can be tailored and adapted based on the observed patient’s behaviour. To address this challenge, we present an AAL framework that monitor activities related to medication adherence.
Hossein Mohammadhassanzadeh; William Van Woensel; Samina Raza Abidi; Syed Sibte Raza Abidi
Semantics-based Plausible Reasoning to Extend the Knowledge Coverage of Medical Knowledge Bases for Improved Clinical Decision Support Journal Article
In: Journal of BioData Mining, vol. 10, no. 7, 2017.
Abstract | Links | BibTeX | Tags: Analogical reasoning, Inductive generalization, Medical knowledge bases, Plausible reasoning, Semantic Web reasoning
@article{Mohammadhassanzadeh2017,
title = {Semantics-based Plausible Reasoning to Extend the Knowledge Coverage of Medical Knowledge Bases for Improved Clinical Decision Support},
author = {Hossein Mohammadhassanzadeh and William Van Woensel and Samina Raza Abidi and Syed Sibte Raza Abidi},
url = {http://rdcu.be/paPY},
doi = {10.1186/s13040-017-0123-y},
year = {2017},
date = {2017-02-10},
journal = {Journal of BioData Mining},
volume = {10},
number = {7},
abstract = {Background
Capturing complete medical knowledge is challenging-often due to incomplete patient Electronic Health Records (EHR), but also because of valuable, tacit medical knowledge hidden away in physicians’ experiences. To extend the coverage of incomplete medical knowledge-based systems beyond their deductive closure, and thus enhance their decision-support capabilities, we argue that innovative, multi-strategy reasoning approaches should be applied. In particular, plausible reasoning mechanisms apply patterns from human thought processes, such as generalization, similarity and interpolation, based on attributional, hierarchical, and relational knowledge. Plausible reasoning mechanisms include inductive reasoning, which generalizes the commonalities among the data to induce new rules, and analogical reasoning, which is guided by data similarities to infer new facts. By further leveraging rich, biomedical Semantic Web ontologies to represent medical knowledge, both known and tentative, we increase the accuracy and expressivity of plausible reasoning, and cope with issues such as data heterogeneity, inconsistency and interoperability. In this paper, we present a Semantic Web-based, multi-strategy reasoning approach, which integrates deductive and plausible reasoning and exploits Semantic Web technology to solve complex clinical decision support queries.
Results
We evaluated our system using a real-world medical dataset of patients with hepatitis, from which we randomly removed different percentages of data (5%, 10%, 15%, and 20%) to reflect scenarios with increasing amounts of incomplete medical knowledge. To increase the reliability of the results, we generated 5 independent datasets for each percentage of missing values, which resulted in 20 experimental datasets (in addition to the original dataset). The results show that plausibly inferred knowledge extends the coverage of the knowledge base by, on average, 2%, 7%, 12%, and 16% for datasets with, respectively, 5%, 10%, 15%, and 20% of missing values. This expansion in the KB coverage allowed solving complex disease diagnostic queries that were previously unresolvable, without losing the correctness of the answers. However, compared to deductive reasoning, data-intensive plausible reasoning mechanisms yield a significant performance overhead.
Conclusions
We observed that plausible reasoning approaches, by generating tentative inferences and leveraging domain knowledge of experts, allow us to extend the coverage of medical knowledge bases, resulting in improved clinical decision support. Second, by leveraging OWL ontological knowledge, we are able to increase the expressivity and accuracy of plausible reasoning methods. Third, our approach is applicable to clinical decision support systems for a range of chronic diseases.},
keywords = {Analogical reasoning, Inductive generalization, Medical knowledge bases, Plausible reasoning, Semantic Web reasoning},
pubstate = {published},
tppubtype = {article}
}
Background
Capturing complete medical knowledge is challenging-often due to incomplete patient Electronic Health Records (EHR), but also because of valuable, tacit medical knowledge hidden away in physicians’ experiences. To extend the coverage of incomplete medical knowledge-based systems beyond their deductive closure, and thus enhance their decision-support capabilities, we argue that innovative, multi-strategy reasoning approaches should be applied. In particular, plausible reasoning mechanisms apply patterns from human thought processes, such as generalization, similarity and interpolation, based on attributional, hierarchical, and relational knowledge. Plausible reasoning mechanisms include inductive reasoning, which generalizes the commonalities among the data to induce new rules, and analogical reasoning, which is guided by data similarities to infer new facts. By further leveraging rich, biomedical Semantic Web ontologies to represent medical knowledge, both known and tentative, we increase the accuracy and expressivity of plausible reasoning, and cope with issues such as data heterogeneity, inconsistency and interoperability. In this paper, we present a Semantic Web-based, multi-strategy reasoning approach, which integrates deductive and plausible reasoning and exploits Semantic Web technology to solve complex clinical decision support queries.
Results
We evaluated our system using a real-world medical dataset of patients with hepatitis, from which we randomly removed different percentages of data (5%, 10%, 15%, and 20%) to reflect scenarios with increasing amounts of incomplete medical knowledge. To increase the reliability of the results, we generated 5 independent datasets for each percentage of missing values, which resulted in 20 experimental datasets (in addition to the original dataset). The results show that plausibly inferred knowledge extends the coverage of the knowledge base by, on average, 2%, 7%, 12%, and 16% for datasets with, respectively, 5%, 10%, 15%, and 20% of missing values. This expansion in the KB coverage allowed solving complex disease diagnostic queries that were previously unresolvable, without losing the correctness of the answers. However, compared to deductive reasoning, data-intensive plausible reasoning mechanisms yield a significant performance overhead.
Conclusions
We observed that plausible reasoning approaches, by generating tentative inferences and leveraging domain knowledge of experts, allow us to extend the coverage of medical knowledge bases, resulting in improved clinical decision support. Second, by leveraging OWL ontological knowledge, we are able to increase the expressivity and accuracy of plausible reasoning methods. Third, our approach is applicable to clinical decision support systems for a range of chronic diseases.
Capturing complete medical knowledge is challenging-often due to incomplete patient Electronic Health Records (EHR), but also because of valuable, tacit medical knowledge hidden away in physicians’ experiences. To extend the coverage of incomplete medical knowledge-based systems beyond their deductive closure, and thus enhance their decision-support capabilities, we argue that innovative, multi-strategy reasoning approaches should be applied. In particular, plausible reasoning mechanisms apply patterns from human thought processes, such as generalization, similarity and interpolation, based on attributional, hierarchical, and relational knowledge. Plausible reasoning mechanisms include inductive reasoning, which generalizes the commonalities among the data to induce new rules, and analogical reasoning, which is guided by data similarities to infer new facts. By further leveraging rich, biomedical Semantic Web ontologies to represent medical knowledge, both known and tentative, we increase the accuracy and expressivity of plausible reasoning, and cope with issues such as data heterogeneity, inconsistency and interoperability. In this paper, we present a Semantic Web-based, multi-strategy reasoning approach, which integrates deductive and plausible reasoning and exploits Semantic Web technology to solve complex clinical decision support queries.
Results
We evaluated our system using a real-world medical dataset of patients with hepatitis, from which we randomly removed different percentages of data (5%, 10%, 15%, and 20%) to reflect scenarios with increasing amounts of incomplete medical knowledge. To increase the reliability of the results, we generated 5 independent datasets for each percentage of missing values, which resulted in 20 experimental datasets (in addition to the original dataset). The results show that plausibly inferred knowledge extends the coverage of the knowledge base by, on average, 2%, 7%, 12%, and 16% for datasets with, respectively, 5%, 10%, 15%, and 20% of missing values. This expansion in the KB coverage allowed solving complex disease diagnostic queries that were previously unresolvable, without losing the correctness of the answers. However, compared to deductive reasoning, data-intensive plausible reasoning mechanisms yield a significant performance overhead.
Conclusions
We observed that plausible reasoning approaches, by generating tentative inferences and leveraging domain knowledge of experts, allow us to extend the coverage of medical knowledge bases, resulting in improved clinical decision support. Second, by leveraging OWL ontological knowledge, we are able to increase the expressivity and accuracy of plausible reasoning methods. Third, our approach is applicable to clinical decision support systems for a range of chronic diseases.
Stefania Costantini; Enrico Franconi; William Van Woensel; Roman Kontchakov; Fariba Sadri; Dumitru Roman (Ed.)
Springer, vol. 10364, 2017, ISBN: 978-3-319-61251-5.
@proceedings{DBLP:conf/ruleml/2017,
title = {Rules and Reasoning - International Joint Conference, RuleML+RR 2017, London, UK, July 12-15, 2017, Proceedings},
editor = {Stefania Costantini and Enrico Franconi and William Van Woensel and Roman Kontchakov and Fariba Sadri and Dumitru Roman},
url = {https://doi.org/10.1007/978-3-319-61252-2},
doi = {10.1007/978-3-319-61252-2},
isbn = {978-3-319-61251-5},
year = {2017},
date = {2017-01-01},
volume = {10364},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
Nick Bassiliades; Antonis Bikakis; Stefania Costantini; Enrico Franconi; Adrian Giurca; Roman Kontchakov; Theodore Patkos; Fariba Sadri; William Van Woensel (Ed.)
CEUR-WS.org, vol. 1875, 2017.
@proceedings{DBLP:conf/ruleml/2017s,
title = {Proceedings of the Doctoral Consortium, Challenge, Industry Track, Tutorials and Posters @ RuleML+RR 2017 hosted by International Joint Conference on Rules and Reasoning 2017 (RuleML+RR 2017), London, UK, July 11-15, 2017},
editor = {Nick Bassiliades and Antonis Bikakis and Stefania Costantini and Enrico Franconi and Adrian Giurca and Roman Kontchakov and Theodore Patkos and Fariba Sadri and William Van Woensel},
url = {http://ceur-ws.org/Vol-1875},
year = {2017},
date = {2017-01-01},
volume = {1875},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
2016
Patrice C. Roy; Samina Raza Abidi; Syed Sibte Raza Abidi
Monitoring Medication Adherence in Smart Environments in the Context of Patient Self-Management: A Knowledge-Driven Approach Book Chapter
In: Bouchard, Bruno; Bouzouane, Abdenour; Guillet, Sébastien (Ed.): Assistive Technologies in Smart Environments for People with Disabilities, CRC Press, Taylor & Francis Group, Boca Raton, FL, 2016, ISBN: 9781498722001.
BibTeX | Tags: Activity Recognition, Self-Management, Smart Homes
@inbook{Roy2016,
title = {Monitoring Medication Adherence in Smart Environments in the Context of Patient Self-Management: A Knowledge-Driven Approach},
author = {Patrice C. Roy and Samina Raza Abidi and Syed Sibte Raza Abidi},
editor = {Bruno Bouchard and Abdenour Bouzouane and Sébastien Guillet},
isbn = {9781498722001},
year = {2016},
date = {2016-09-15},
booktitle = {Assistive Technologies in Smart Environments for People with Disabilities},
publisher = {CRC Press, Taylor & Francis Group},
address = {Boca Raton, FL},
keywords = {Activity Recognition, Self-Management, Smart Homes},
pubstate = {published},
tppubtype = {inbook}
}
Wasif Hasan Baig
A Semantic Web Based Knowledge Management Framework to Model Behaviour Change Constructs for Generation of Personalized Action Plans Masters Thesis
Dalhousie University, 2016.
Links | BibTeX | Tags: Behavioural Theory, Chronic Illness, Knowledge Modelling, Ontology Engineering, Patient Empowerment, Personalized Medicine, Self-Management, Semantic Web, Social Cognition Theory
@mastersthesis{Baig-Wasif-MHI-HINF,
title = {A Semantic Web Based Knowledge Management Framework to Model Behaviour Change Constructs for Generation of Personalized Action Plans},
author = {Wasif Hasan Baig},
url = {https://niche.cs.dal.ca/wp-content/uploads/2016/01/Baig-Wasif-MHI-HINF-September-2015.pdf},
year = {2016},
date = {2016-09-01},
school = {Dalhousie University},
keywords = {Behavioural Theory, Chronic Illness, Knowledge Modelling, Ontology Engineering, Patient Empowerment, Personalized Medicine, Self-Management, Semantic Web, Social Cognition Theory},
pubstate = {published},
tppubtype = {mastersthesis}
}
Suria Kala Subbu
A Semantic Web Framework For Representing, Linking And Analyzing Medical Data For Optimizing Laboratory Utilization Journal Article
In: 2016.
Abstract | Links | BibTeX | Tags: Health Data Analytics, Health Informatics, Knowledge Based Systems, Knowledge Linkage, Knowledge Management, Ontology Engineering, Semantic Web
@article{Subbu16,
title = {A Semantic Web Framework For Representing, Linking And Analyzing Medical Data For Optimizing Laboratory Utilization},
author = {Suria Kala Subbu},
url = {https://dalspace.library.dal.ca/xmlui/handle/10222/72131?show=full},
year = {2016},
date = {2016-09-01},
abstract = {In this thesis, we investigate semantic web based methods for representing, linking and analyzing medical data. The main challenge addressed in this work is the transformation of data stored in a relational database to an ontological model that allows to represent as RDF triples and to link the data with external data sources using linked data principles. We have implemented a semantic analytics framework that comprises the following elements: (a) Domain-specific ontology to represent the data model and data inference. (b) RDMS data extraction using a domain-specific ontology (TBOX) based on the relational database schema; (c) Ontology instantiation (ABOX) that involves converting the relational data in terms of RDF triples. A key feature of our approach is the data is not physically migrated from the RDBS to RDF, rather we dynamically materialize the RDF triples thus avoiding the creation of a large RDF dump; (d) Linking the RDF data with available open data in RDF format using ontology-based concept alignments; and (e) Semantic analytics using SPARQL to identify semantically-salient patterns within the data. We have applied our semantic analytics data to analyze pathology lab data (over 5 years of pathology order data), where we were able to identify prevalent order-sets inherent within the data, and we also evaluated the change in the frequent order-sets over a five year time period.},
keywords = {Health Data Analytics, Health Informatics, Knowledge Based Systems, Knowledge Linkage, Knowledge Management, Ontology Engineering, Semantic Web},
pubstate = {published},
tppubtype = {article}
}
In this thesis, we investigate semantic web based methods for representing, linking and analyzing medical data. The main challenge addressed in this work is the transformation of data stored in a relational database to an ontological model that allows to represent as RDF triples and to link the data with external data sources using linked data principles. We have implemented a semantic analytics framework that comprises the following elements: (a) Domain-specific ontology to represent the data model and data inference. (b) RDMS data extraction using a domain-specific ontology (TBOX) based on the relational database schema; (c) Ontology instantiation (ABOX) that involves converting the relational data in terms of RDF triples. A key feature of our approach is the data is not physically migrated from the RDBS to RDF, rather we dynamically materialize the RDF triples thus avoiding the creation of a large RDF dump; (d) Linking the RDF data with available open data in RDF format using ontology-based concept alignments; and (e) Semantic analytics using SPARQL to identify semantically-salient patterns within the data. We have applied our semantic analytics data to analyze pathology lab data (over 5 years of pathology order data), where we were able to identify prevalent order-sets inherent within the data, and we also evaluated the change in the frequent order-sets over a five year time period.
Syed Sibte Raza Abidi; Abhinav Kumar Singh; Sean Christie
Transcription of Case Report Forms from Unstructured Referral Letters: A Semantic Text Analytics Approach Proceedings Article
In: Exploring Complexity in Health: An Interdisciplinary Systems Approach. 26th European Medical Informatics Conference (MIE2016), Munich, pp. 322-326, IOS Press, 2016.
Abstract | Links | BibTeX | Tags: Referral letters, semantic mapping, Unstructured text analysis
@inproceedings{Abidi2016b,
title = {Transcription of Case Report Forms from Unstructured Referral Letters: A Semantic Text Analytics Approach},
author = {Syed Sibte Raza Abidi and Abhinav Kumar Singh and Sean Christie},
doi = {10.3233/978-1-61499-678-1-322},
year = {2016},
date = {2016-08-15},
booktitle = {Exploring Complexity in Health: An Interdisciplinary Systems Approach. 26th European Medical Informatics Conference (MIE2016), Munich},
volume = {228},
pages = {322-326},
publisher = {IOS Press},
series = {Studies in Health Technology and Informatics},
abstract = {In this paper we present a framework for the semi-automatic extraction of medical entities from referral letters and use them to transcribe a case report form. Our framework offers the functionality to: (a) extract the medical entity from the unstructured referral letters, (b) classify them according to their semantic type, and (c) transcribe a case report form based on the extracted information from the referral letter. We take a semantic text analytics approach where SNOMED-CT ontology is used to both classify referral concepts and to establish semantic similarities between referral concepts and CRF elements. We used 100 spine injury referral letters, and a standard case report form used by Association of Dalhousie Neurosurgeons, Dalhousie University},
keywords = {Referral letters, semantic mapping, Unstructured text analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we present a framework for the semi-automatic extraction of medical entities from referral letters and use them to transcribe a case report form. Our framework offers the functionality to: (a) extract the medical entity from the unstructured referral letters, (b) classify them according to their semantic type, and (c) transcribe a case report form based on the extracted information from the referral letter. We take a semantic text analytics approach where SNOMED-CT ontology is used to both classify referral concepts and to establish semantic similarities between referral concepts and CRF elements. We used 100 spine injury referral letters, and a standard case report form used by Association of Dalhousie Neurosurgeons, Dalhousie University
Wasif Hasan Baig; Samina Abidi; Syed Sibte Raza Abidi
An Ontological Model of Behaviour Theory to Generate Personalized Action Plans to Modify Behaviours Proceedings Article
In: Exploring Complexity in Health: An Interdisciplinary Systems Approach. 26th European Medical Informatics Conference (MIE2016), Munich, pp. 399-403, IOS Press, 2016.
Abstract | Links | BibTeX | Tags: Action Plans, Behaviour Modelling, Information Personalization, Ontology
@inproceedings{Baig2016b,
title = {An Ontological Model of Behaviour Theory to Generate Personalized Action Plans to Modify Behaviours},
author = {Wasif Hasan Baig and Samina Abidi and Syed Sibte Raza Abidi},
doi = {10.3233/978-1-61499-678-1-399},
year = {2016},
date = {2016-08-15},
booktitle = {Exploring Complexity in Health: An Interdisciplinary Systems Approach. 26th European Medical Informatics Conference (MIE2016), Munich},
volume = {228},
pages = {399-403},
publisher = {IOS Press},
series = {Studies in Health Technology and Informatics},
abstract = {Behavior change approaches aim to assist patients in achieving self-efficacy in managing their condition. Social cognitive theory (SCT) stipulates self-efficacy as a central element to behavior change and provides constructs to achieve self-efficacy guided by person-specific action plans. In our work, to administer behaviour change in patient with chronic conditions, our approach entails the computerization of SCT-based self-efficacy constructs in order to generate personalized action plans that are suitable to an individual's current care scenario. We have taken a knowledge management approach, whereby we have computerized the SCT-based self-efficacy constructs in terms of a high-level SCT knowledge model that can be operationalized to generate personalized behaviour change action plans. We have collected and computerized behavior change content targeting healthy living and physical activity. Semantic web technologies have been used to develop the SCT knowledge model, represented in terms of an ontology and SWRL rules. The ontological SCT model can inferred to generate personalized self-management action plans for a given patient profile. We present formative evaluation of the clinical correctness and relevance of the generated personalized action plans for a range of test patient profiles},
keywords = {Action Plans, Behaviour Modelling, Information Personalization, Ontology},
pubstate = {published},
tppubtype = {inproceedings}
}
Behavior change approaches aim to assist patients in achieving self-efficacy in managing their condition. Social cognitive theory (SCT) stipulates self-efficacy as a central element to behavior change and provides constructs to achieve self-efficacy guided by person-specific action plans. In our work, to administer behaviour change in patient with chronic conditions, our approach entails the computerization of SCT-based self-efficacy constructs in order to generate personalized action plans that are suitable to an individual's current care scenario. We have taken a knowledge management approach, whereby we have computerized the SCT-based self-efficacy constructs in terms of a high-level SCT knowledge model that can be operationalized to generate personalized behaviour change action plans. We have collected and computerized behavior change content targeting healthy living and physical activity. Semantic web technologies have been used to develop the SCT knowledge model, represented in terms of an ontology and SWRL rules. The ontological SCT model can inferred to generate personalized self-management action plans for a given patient profile. We present formative evaluation of the clinical correctness and relevance of the generated personalized action plans for a range of test patient profiles
Samina Raza Abidi; Jafna Cox; Ashraf Abusharekh; Nima Hashemian; Syed Sibte Raza Abidi
A Digital Health System to Assist Family Physicians to Safely Prescribe NOAC Medication Proceedings Article
In: Exploring Complexity in Health: An Interdisciplinary Systems Approach. 26th European Medical Informatics Conference (MIE2016), Munich, pp. 519-523, IOS Press, 2016.
Abstract | Links | BibTeX | Tags: Clinical Decision Support Systems, Clinical Practice Guidelines, Digital Health
@inproceedings{Abidi2016b,
title = {A Digital Health System to Assist Family Physicians to Safely Prescribe NOAC Medication},
author = {Samina Raza Abidi and Jafna Cox and Ashraf Abusharekh and Nima Hashemian and Syed Sibte Raza Abidi},
doi = {10.3233/978-1-61499-678-1-519},
year = {2016},
date = {2016-08-15},
booktitle = {Exploring Complexity in Health: An Interdisciplinary Systems Approach. 26th European Medical Informatics Conference (MIE2016), Munich},
volume = {228},
pages = {519-523},
publisher = {IOS Press},
series = {Studies in Health Technology and Informatics},
abstract = {Atrial Fibrillation (AF) is the most common cardiac arrhythmia. Generally, the therapeutic options for managing AF include the use of anticoagulant drugs that prevent the coagulation of blood. New Oral Anticoagulants (NOACs) are not optimally prescribed to patients, despite their efficacy. In Canada, NOAC medications are not directly available to patients who belong to provincial benefits programs, rather a NOAC special authorization process establishes the eligibility of a patient to receive a NOAC and be paid by the provincial Pharmacare program. This special authorization process is tedious and paper-based which inhibits physicians to prescribe NOAC leading to suboptimal AF care to patients. In this paper, we present a computerized NOAC Authorization Decision Support System (NOAC-ADSS), accessible to physicians to help them (a) determine a patient eligibility for NOAC based on Canadian AF clinical guidelines, and (b) complete the special authorization form. We present a semantic web based system to ontologically model the NOAC eligibility criteria and execute the knowledge to determine a patient NOAC eligibility and dosage},
keywords = {Clinical Decision Support Systems, Clinical Practice Guidelines, Digital Health},
pubstate = {published},
tppubtype = {inproceedings}
}
Atrial Fibrillation (AF) is the most common cardiac arrhythmia. Generally, the therapeutic options for managing AF include the use of anticoagulant drugs that prevent the coagulation of blood. New Oral Anticoagulants (NOACs) are not optimally prescribed to patients, despite their efficacy. In Canada, NOAC medications are not directly available to patients who belong to provincial benefits programs, rather a NOAC special authorization process establishes the eligibility of a patient to receive a NOAC and be paid by the provincial Pharmacare program. This special authorization process is tedious and paper-based which inhibits physicians to prescribe NOAC leading to suboptimal AF care to patients. In this paper, we present a computerized NOAC Authorization Decision Support System (NOAC-ADSS), accessible to physicians to help them (a) determine a patient eligibility for NOAC based on Canadian AF clinical guidelines, and (b) complete the special authorization form. We present a semantic web based system to ontologically model the NOAC eligibility criteria and execute the knowledge to determine a patient NOAC eligibility and dosage
Soude Fazeli; Enayat Rajabi; Leonardo Lezcano; Hendrik Drachsler; Peter Sloep
Supporting Users of Open Online Courses with Recommendations: an Algorithmic Study Proceedings Article
In: Advanced Learning Technologies (ICALT), 2016 IEEE 16th International Conference on, Austin, TX, USA, IEEE , 2016, ISSN: 2161-377X.
Abstract | Links | BibTeX | Tags: Learning technology
@inproceedings{Fazeli2016,
title = {Supporting Users of Open Online Courses with Recommendations: an Algorithmic Study},
author = {Soude Fazeli and Enayat Rajabi and Leonardo Lezcano and Hendrik Drachsler and Peter Sloep
},
doi = {10.1109/ICALT.2016.119},
issn = {2161-377X},
year = {2016},
date = {2016-07-25},
booktitle = {Advanced Learning Technologies (ICALT), 2016 IEEE 16th International Conference on, Austin, TX, USA},
publisher = {IEEE },
abstract = {Almost all studies on course recommenders in online platforms target closed online platforms that belong to a University or other provider. Recently, a demand has developed that targets open platforms. Such platforms lack rich user profiles with content metadata. Instead they log user interactions. We report on how user interactions and activities tracked in open online learning platforms may generate recommendations. We use data from the OpenU open online learning platform in use by the Open University of the Netherlands to investigate the application of several state-of-the-art recommender algorithms, including a graph-based recommender approach. It appears that user-based and memory-based methods perform better than model-based and factorization methods. Particularly, the graph-based recommender system outperforms the classical approaches on prediction accuracy of recommendations in terms of recall.
},
keywords = {Learning technology},
pubstate = {published},
tppubtype = {inproceedings}
}
Almost all studies on course recommenders in online platforms target closed online platforms that belong to a University or other provider. Recently, a demand has developed that targets open platforms. Such platforms lack rich user profiles with content metadata. Instead they log user interactions. We report on how user interactions and activities tracked in open online learning platforms may generate recommendations. We use data from the OpenU open online learning platform in use by the Open University of the Netherlands to investigate the application of several state-of-the-art recommender algorithms, including a graph-based recommender approach. It appears that user-based and memory-based methods perform better than model-based and factorization methods. Particularly, the graph-based recommender system outperforms the classical approaches on prediction accuracy of recommendations in terms of recall.
Enayat Rajabi; Seyed-Mehdi-Reza Beheshti
Interlinking Big Data to Web of Data Book Chapter
In: vol. 18, pp. 133-145, Springer International Publishing, 2016, ISBN: 978-3-319-30263-8.
Abstract | Links | BibTeX | Tags: Big Data, Linked Data
@inbook{Rajabi2016,
title = {Interlinking Big Data to Web of Data},
author = {Enayat Rajabi and Seyed-Mehdi-Reza Beheshti},
doi = {10.1007/978-3-319-30265-2_6},
isbn = {978-3-319-30263-8},
year = {2016},
date = {2016-05-27},
volume = {18},
pages = {133-145},
publisher = {Springer International Publishing},
abstract = {The big data problem can be seen as a massive number of data islands, ranging from personal, shared, social to business data. The data in these islands is getting large scale, never ending, and ever changing, arriving in batches at irregular time intervals. Examples of these are social and business data. Linking and analyzing of this potentially connected data is of high and valuable interest. In this context, it will be important to investigate how the Linked Data approach can enable the Big Data optimization. In particular, the Linked Data approach has recently facilitated the accessibility, sharing, and enrichment of data on the Web. Scientists believe that Linked Data reduces Big Data variability by some of the scientifically less interesting dimensions. In particular, by applying the Linked Data techniques for exposing structured data and eventually interlinking them to useful knowledge on the Web, many syntactic issues vanish. Generally speaking, this approach improves data optimization by providing some solutions for intelligent and automatic linking among datasets. In this chapter, we aim to discuss the advantages of applying the Linked Data approach, towards the optimization of Big Data in the Linked Open Data (LOD) cloud by: (i) describing the impact of linking Big Data to LOD cloud; (ii) representing various interlinking tools for linking Big Data; and (iii) providing a practical case study: linking a very large dataset to DBpedia.
},
keywords = {Big Data, Linked Data},
pubstate = {published},
tppubtype = {inbook}
}
The big data problem can be seen as a massive number of data islands, ranging from personal, shared, social to business data. The data in these islands is getting large scale, never ending, and ever changing, arriving in batches at irregular time intervals. Examples of these are social and business data. Linking and analyzing of this potentially connected data is of high and valuable interest. In this context, it will be important to investigate how the Linked Data approach can enable the Big Data optimization. In particular, the Linked Data approach has recently facilitated the accessibility, sharing, and enrichment of data on the Web. Scientists believe that Linked Data reduces Big Data variability by some of the scientifically less interesting dimensions. In particular, by applying the Linked Data techniques for exposing structured data and eventually interlinking them to useful knowledge on the Web, many syntactic issues vanish. Generally speaking, this approach improves data optimization by providing some solutions for intelligent and automatic linking among datasets. In this chapter, we aim to discuss the advantages of applying the Linked Data approach, towards the optimization of Big Data in the Linked Open Data (LOD) cloud by: (i) describing the impact of linking Big Data to LOD cloud; (ii) representing various interlinking tools for linking Big Data; and (iii) providing a practical case study: linking a very large dataset to DBpedia.
Hossein Mohammadhassanzadeh; William Van Woensel; Samina Raza Abidi; Syed Sibte Raza Abidi
A Semantic Web-based Approach to Plausible Reasoning for Improving Clinical Knowledge Engineering Proceedings Article
In: IEEE International Conference on Biomedical and Health Informatics, Las Vegas, 2016.
Abstract | BibTeX | Tags: Clinical Decision Support Systems, Plausbile Reasoning, Semantic Web
@inproceedings{Mohammadhassanzadeh2016,
title = {A Semantic Web-based Approach to Plausible Reasoning for Improving Clinical Knowledge Engineering},
author = {Hossein Mohammadhassanzadeh and William Van Woensel and Samina Raza Abidi and Syed Sibte Raza Abidi},
year = {2016},
date = {2016-02-24},
urldate = {2016-02-24},
booktitle = {IEEE International Conference on Biomedical and Health Informatics, Las Vegas},
abstract = {In this paper, we present a semantic web based knowledge engineering approach to extend the coverage of medical knowledge-based systems in order to solve complex medical queries that demand the integration of deterministic and plausible knowledge. We leverage plausible reasoning mechanisms, which exploit associations between the underlying domain-specific data, as well as tentative domain knowledge, to extend the coverage of a medical knowledge base. We demonstrate that Semantic Web technologies, due to their efficient solutions for federated data management and built-in DL-based inferencing methods, offer useful opportunities to support plausible reasoning for medical decision support tasks. We evaluated our multi-strategy medical reasoning approach using real-world medical data. Our results illustrate that plausible reasoning improved the knowledge coverage of the original medical knowledge base by 10-12%, and in turn helped to solve complex disease diagnostic queries.},
keywords = {Clinical Decision Support Systems, Plausbile Reasoning, Semantic Web},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we present a semantic web based knowledge engineering approach to extend the coverage of medical knowledge-based systems in order to solve complex medical queries that demand the integration of deterministic and plausible knowledge. We leverage plausible reasoning mechanisms, which exploit associations between the underlying domain-specific data, as well as tentative domain knowledge, to extend the coverage of a medical knowledge base. We demonstrate that Semantic Web technologies, due to their efficient solutions for federated data management and built-in DL-based inferencing methods, offer useful opportunities to support plausible reasoning for medical decision support tasks. We evaluated our multi-strategy medical reasoning approach using real-world medical data. Our results illustrate that plausible reasoning improved the knowledge coverage of the original medical knowledge base by 10-12%, and in turn helped to solve complex disease diagnostic queries.
William Van Woensel; Sven Casteleyn
A Mobile Query Service for Integrated Access to Large Numbers of Online Semantic Web Data Sources Journal Article
In: Web Semant., vol. 36, no. C, pp. 58–76, 2016, ISSN: 1570-8268.
Links | BibTeX | Tags: Cache replacement, Data caching, Data indexing, Data integration, Mobile Computing, Open world assumption
@article{VanWoensel:2016:MQS:2895617.2895676,
title = {A Mobile Query Service for Integrated Access to Large Numbers of Online Semantic Web Data Sources},
author = { William Van Woensel and Sven Casteleyn},
url = {https://niche.cs.dal.ca/wp-content/uploads/2016/05/A-Mobile-Query-Service-for-Integrated-Access-to-Large-Numbers-of-Online-Semantic-Web-Data-Sources-1.pdf},
doi = {10.1016/j.websem.2015.10.002},
issn = {1570-8268},
year = {2016},
date = {2016-01-01},
journal = {Web Semant.},
volume = {36},
number = {C},
pages = {58--76},
publisher = {Elsevier Science Publishers B. V.},
address = {Amsterdam, The Netherlands, The Netherlands},
keywords = {Cache replacement, Data caching, Data indexing, Data integration, Mobile Computing, Open world assumption},
pubstate = {published},
tppubtype = {article}
}
IEEE, 2016, ISBN: 978-1-5090-2455-1.
@proceedings{DBLP:conf/bhi/2016,
title = {2016 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2016, Las Vegas, NV, USA, February 24-27, 2016},
url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7449722},
isbn = {978-1-5090-2455-1},
year = {2016},
date = {2016-01-01},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
William Van Woensel; Patrice C. Roy; Syed Sibte Raza Abidi
SmartRL: A Context-Sensitive, Ontology-Based Rule Language for Assisted Living in Smart Environments Proceedings Article
In: Rule Technologies. Research, Tools, and Applications - 10th International Symposium, RuleML 2016, Stony Brook, NY, USA, July 6-9, 2016. Proceedings, pp. 341–349, 2016.
@inproceedings{DBLP:conf/ruleml/WoenselRA16,
title = {SmartRL: A Context-Sensitive, Ontology-Based Rule Language for Assisted Living in Smart Environments},
author = {William Van Woensel and Patrice C. Roy and Syed Sibte Raza Abidi},
url = {http://dx.doi.org/10.1007/978-3-319-42019-6_22},
doi = {10.1007/978-3-319-42019-6_22},
year = {2016},
date = {2016-01-01},
booktitle = {Rule Technologies. Research, Tools, and Applications - 10th International Symposium, RuleML 2016, Stony Brook, NY, USA, July 6-9, 2016. Proceedings},
pages = {341--349},
crossref = {DBLP:conf/ruleml/2016},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}