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Research Interests
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Publications
2017
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}
}
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.
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}
}
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
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.
Borna Jafarpour; Syed Sibte Raza Abidi; Samina Raza Abidi
Exploiting Semantic Web Technologies to Develop OWL-Based Clinical Practice Guideline Execution Engines Journal Article
In: IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 1, pp. 388-398, 2016, ISSN: 2168-2194.
Abstract | Links | BibTeX | Tags: Clinical Decision Support Systems, Clinical Practice Guidelines, OWL, Semantic Web
@article{Jafarpour:IeeeJournalOfBiomedicalAndHealthInformat,
title = {Exploiting Semantic Web Technologies to Develop OWL-Based Clinical Practice Guideline Execution Engines},
author = {Borna Jafarpour and Syed Sibte Raza Abidi and Samina Raza Abidi},
url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6990486&isnumber=7369993},
doi = {10.1109/JBHI.2014.2383840},
issn = {2168-2194},
year = {2016},
date = {2016-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
volume = {20},
number = {1},
pages = {388-398},
abstract = {Computerizing paper-based CPG and then executing them can provide evidence-informed decision support to physicians at the point of care. Semantic web technologies especially Web Ontology Language (OWL) ontologies have been profusely used to represent computerized CPG. Using semantic web reasoning capabilities to execute OWL based computerized CPG unties them from a specific custom-built CPG execution engine and increases their shareability as any OWL reasoner and triple store can be utilized for CPG execution. However, existing semantic web reasoning based CPG execution engines suffer from lack of ability to execute CPG with high levels of expressivity, high cognitive load of computerization of paper-based CPG and updating their computerized versions. In order to address these limitations, we have developed three CPG execution engines based on OWL 1 DL, OWL 2 DL and OWL 2 DL + Semantic Web Rule Language (SWRL). OWL 1 DL serves as the base execution engine capable of executing a wide range of CPG constructs, however for executing highly complex CPG the OWL 2 DL and OWL 2 DL + SWRL offer additional executional capabilities. We evaluated the technical performance and medical correctness of our execution engines using a range of CPG. Technical evaluations show the efficiency of our CPG execution engines in terms of CPU time and validity of the generated recommendation in comparison to existing CPG execution engines. Medical evaluations by domain experts show the validity of the CPG-mediated therapy plans in terms of relevance, safety, and ordering for a wide range of patient scenarios.},
keywords = {Clinical Decision Support Systems, Clinical Practice Guidelines, OWL, Semantic Web},
pubstate = {published},
tppubtype = {article}
}
Computerizing paper-based CPG and then executing them can provide evidence-informed decision support to physicians at the point of care. Semantic web technologies especially Web Ontology Language (OWL) ontologies have been profusely used to represent computerized CPG. Using semantic web reasoning capabilities to execute OWL based computerized CPG unties them from a specific custom-built CPG execution engine and increases their shareability as any OWL reasoner and triple store can be utilized for CPG execution. However, existing semantic web reasoning based CPG execution engines suffer from lack of ability to execute CPG with high levels of expressivity, high cognitive load of computerization of paper-based CPG and updating their computerized versions. In order to address these limitations, we have developed three CPG execution engines based on OWL 1 DL, OWL 2 DL and OWL 2 DL + Semantic Web Rule Language (SWRL). OWL 1 DL serves as the base execution engine capable of executing a wide range of CPG constructs, however for executing highly complex CPG the OWL 2 DL and OWL 2 DL + SWRL offer additional executional capabilities. We evaluated the technical performance and medical correctness of our execution engines using a range of CPG. Technical evaluations show the efficiency of our CPG execution engines in terms of CPU time and validity of the generated recommendation in comparison to existing CPG execution engines. Medical evaluations by domain experts show the validity of the CPG-mediated therapy plans in terms of relevance, safety, and ordering for a wide range of patient scenarios.
2015
William Van Woensel; Hossein Mohammadhassanzadeh; Samina Raza Abidi; Syed Sibte Raza Abidi
Multi-Strategy Semantic Web Reasoning for Medical Knowledge Bases Proceedings Article
In: Proceedings of International Workshop on Biomedical Data Mining, Modeling, and Semantic Integration: A Promising Approach to Solving Unmet Medical Needs (BDM2I2015), 2015.
Links | BibTeX | Tags: Health Informatics, Knowledge Based Systems, Ontology, Semantic Web
@inproceedings{DBLP:conf/semweb/WoenselMAA15,
title = {Multi-Strategy Semantic Web Reasoning for Medical Knowledge Bases},
author = {William Van Woensel and Hossein Mohammadhassanzadeh and Samina Raza Abidi and Syed Sibte Raza Abidi},
url = {http://ceur-ws.org/Vol-1428/BDM2I_2015_paper_8.pdf},
year = {2015},
date = {2015-10-11},
booktitle = {Proceedings of International Workshop on Biomedical Data Mining, Modeling, and Semantic Integration: A Promising Approach to Solving Unmet Medical Needs (BDM2I2015)},
keywords = {Health Informatics, Knowledge Based Systems, Ontology, Semantic Web},
pubstate = {published},
tppubtype = {inproceedings}
}
Amina Russell; William Van Woensel; Samina Raza Abidi
The Shared Decision Making Frontier: Managing Non-Critical Chronic Illness by Combining Behavioural & Decision Theory with Online Technology Proceedings Article
In: MEDINFO 2015: eHealth-enabled Health, 2015.
Abstract | Links | BibTeX | Tags: Biomedical Technology Intervention, Choice Architecture, Chronic Illness, Shared Decision Making
@inproceedings{arussell-medinfo15,
title = {The Shared Decision Making Frontier: Managing Non-Critical Chronic Illness by Combining Behavioural & Decision Theory with Online Technology},
author = {Amina Russell and William Van Woensel and Samina Raza Abidi},
url = {http://web.cs.dal.ca/~woensel/paper/The Shared Decision Making Frontier a Feasibility and Usability Study for Managing Non-Critical Chronic Illness by Combining Behavioural & Decision Theory with Online Technology.pdf},
year = {2015},
date = {2015-09-19},
booktitle = {MEDINFO 2015: eHealth-enabled Health},
abstract = {Objective: To determine if shared decisions for managing non-critical chronic illness, made through an online biomedical technology intervention, proves feasible and usable. The technology intervention incorporates behavioural and decision theory to increase patient engagement, and ultimately long term adherence to health behaviour change.
Method: We devised the iheart web intervention as a “proof of concept” in five phases: 1) conceptual, 2) design, 3) application development, 4) testing and 5) study assessment. The implementation incorporates the Vaadin web application framework, Drools, EclipseLink and a MySQL database.
Results and discussion: Two-thirds of the study participants favoured the technology intervention, based on Likert-scale questions from a post-study questionnaire. Qualitative analysis of think aloud feedback, video screen captures and open-ended questions from the post-study questionnaire, uncovered six main areas or themes for improvement.
Conclusion: Online shared decisions for managing a non-critical chronic illness proved feasible and usable through the iheart web intervention. Areas needing improvement have been identified for the next application revision. An efficacy study is recommended as a next step.
},
keywords = {Biomedical Technology Intervention, Choice Architecture, Chronic Illness, Shared Decision Making},
pubstate = {published},
tppubtype = {inproceedings}
}
Objective: To determine if shared decisions for managing non-critical chronic illness, made through an online biomedical technology intervention, proves feasible and usable. The technology intervention incorporates behavioural and decision theory to increase patient engagement, and ultimately long term adherence to health behaviour change.
Method: We devised the iheart web intervention as a “proof of concept” in five phases: 1) conceptual, 2) design, 3) application development, 4) testing and 5) study assessment. The implementation incorporates the Vaadin web application framework, Drools, EclipseLink and a MySQL database.
Results and discussion: Two-thirds of the study participants favoured the technology intervention, based on Likert-scale questions from a post-study questionnaire. Qualitative analysis of think aloud feedback, video screen captures and open-ended questions from the post-study questionnaire, uncovered six main areas or themes for improvement.
Conclusion: Online shared decisions for managing a non-critical chronic illness proved feasible and usable through the iheart web intervention. Areas needing improvement have been identified for the next application revision. An efficacy study is recommended as a next step.
Method: We devised the iheart web intervention as a “proof of concept” in five phases: 1) conceptual, 2) design, 3) application development, 4) testing and 5) study assessment. The implementation incorporates the Vaadin web application framework, Drools, EclipseLink and a MySQL database.
Results and discussion: Two-thirds of the study participants favoured the technology intervention, based on Likert-scale questions from a post-study questionnaire. Qualitative analysis of think aloud feedback, video screen captures and open-ended questions from the post-study questionnaire, uncovered six main areas or themes for improvement.
Conclusion: Online shared decisions for managing a non-critical chronic illness proved feasible and usable through the iheart web intervention. Areas needing improvement have been identified for the next application revision. An efficacy study is recommended as a next step.
William Van Woensel; Patrice C. Roy; Samina Raza Abidi; Syed Sibte Raza Abidi
A Mobile and Intelligent Patient Diary for Chronic Disease Self-Management Proceedings Article
In: MEDINFO 2015: eHealth-enabled Health - Proceedings of the 15th World Congress on Health and Biomedical Informatics, Sao Paulo, Brazil, pp. 118–122, IOS Press, 2015, ISBN: 978-1-61499-563-0.
Links | BibTeX | Tags: Chronic Disease Management, Mobile Health, Patient Diary, Patient Empowerment, Self-Management
@inproceedings{DBLP:conf/medinfo/WoenselRAA15,
title = {A Mobile and Intelligent Patient Diary for Chronic Disease Self-Management},
author = {William Van Woensel and Patrice C. Roy and Samina Raza Abidi and Syed Sibte Raza Abidi},
url = {http://dx.doi.org/10.3233/978-1-61499-564-7-118},
doi = {10.3233/978-1-61499-564-7-118},
isbn = {978-1-61499-563-0},
year = {2015},
date = {2015-08-23},
booktitle = {MEDINFO 2015: eHealth-enabled Health - Proceedings of the 15th World Congress on Health and Biomedical Informatics, Sao Paulo, Brazil},
pages = {118--122},
publisher = {IOS Press},
keywords = {Chronic Disease Management, Mobile Health, Patient Diary, Patient Empowerment, Self-Management},
pubstate = {published},
tppubtype = {inproceedings}
}
Borna Jafarpour; Samina Raza Abidi; Ahmad Marwan Ahmad; Syed Sibte Raza Abidi
INITIATE: An Intelligent Adaptive Alert Environment Proceedings Article
In: MEDINFO 2015: eHealth-enabled Health - Proceedings of the 15th World Congress on Health and Biomedical Informatics, Sao Paulo, Brazil, pp. 285–289, IOS Press, 2015, ISBN: 978-1-61499-563-0.
Links | BibTeX | Tags: Alert Fatigue, Clinical Decision Support Systems, Knowledge Management
@inproceedings{DBLP:conf/medinfo/JafarpourAAA15,
title = {INITIATE: An Intelligent Adaptive Alert Environment},
author = {Borna Jafarpour and Samina Raza Abidi and Ahmad Marwan Ahmad and Syed Sibte Raza Abidi},
url = {http://dx.doi.org/10.3233/978-1-61499-564-7-285},
doi = {10.3233/978-1-61499-564-7-285},
isbn = {978-1-61499-563-0},
year = {2015},
date = {2015-08-23},
booktitle = {MEDINFO 2015: eHealth-enabled Health - Proceedings of the 15th World Congress on Health and Biomedical Informatics, Sao Paulo, Brazil},
pages = {285--289},
publisher = {IOS Press},
keywords = {Alert Fatigue, Clinical Decision Support Systems, Knowledge Management},
pubstate = {published},
tppubtype = {inproceedings}
}
0000
William Van Woensel; Samina Raza Abidi; Syed Sibte Raza Abidi
Decision Support for Comorbid Conditions via Execution-Time Integration of Clinical Guidelines Using Transaction-based Semantics and Temporal Planning (under review) Journal Article
In: Artificial Intelligence in Medicine, 0000.
BibTeX | Tags: Clinical Decision Support Systems, Clinical Practice Guidelines, Comorbidity
@article{wvw_aiim_21,
title = {Decision Support for Comorbid Conditions via Execution-Time Integration of Clinical Guidelines Using Transaction-based Semantics and Temporal Planning (under review)},
author = {William Van Woensel and Samina Raza Abidi and Syed Sibte Raza Abidi},
journal = {Artificial Intelligence in Medicine},
keywords = {Clinical Decision Support Systems, Clinical Practice Guidelines, Comorbidity},
pubstate = {published},
tppubtype = {article}
}