About Me
About you ..
Research Interests
Your research interests.
Publications
2024
Jaber Rad; Karthik Tennankore; Samina Abidi; Syed Sibte Raza Abidi
Extracting Decision Paths via Surrogate Modeling for Explainability of Black Box Classifiers Proceedings Article Forthcoming
In: 11th IEEE Swiss Conference on Data Science (SDS2024), 30-31 May 2024, Zurich, Switzerland, 2024, Forthcoming.
BibTeX | Tags:
@inproceedings{nokey,
title = {Extracting Decision Paths via Surrogate Modeling for Explainability of Black Box Classifiers},
author = {Jaber Rad and Karthik Tennankore and Samina Abidi and Syed Sibte Raza Abidi},
year = {2024},
date = {2024-03-30},
booktitle = {11th IEEE Swiss Conference on Data Science (SDS2024), 30-31 May 2024, Zurich, Switzerland, 2024},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
Hossein Mohammadhassanzadeh; Samina Raza Abidi; Syed Sibte Raza Abidi
Plausible Reasoning over Large Health Datasets: A Novel Approach to Data Analytics Leveraging Semantics Journal Article
In: Knowledge-Based Systems, 2024.
@article{nokey,
title = {Plausible Reasoning over Large Health Datasets: A Novel Approach to Data Analytics Leveraging Semantics},
author = {Hossein Mohammadhassanzadeh and Samina Raza Abidi and Syed Sibte Raza Abidi},
doi = {10.1016/j.knosys.2024.111493},
year = {2024},
date = {2024-02-11},
urldate = {2024-02-11},
journal = {Knowledge-Based Systems},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2023
Kyle W. Eastwood; Ronald May; Pantelis Andreou; Samina Abidi; Syed Sibte Raza Abidi; Osama M. Loubani
Needs and expectations for artificial intelligence in emergency medicine according to Canadian physicians Journal Article
In: BMC Health Services Research, vol. 23, no. 798, 2023, ISSN: 1472-6963.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Needs and expectations for artificial intelligence in emergency medicine according to Canadian physicians},
author = {Kyle W. Eastwood and Ronald May and Pantelis Andreou and Samina Abidi and Syed Sibte Raza Abidi and Osama M. Loubani},
doi = {https://doi.org/10.1186/s12913-023-09740-w},
issn = {1472-6963},
year = {2023},
date = {2023-07-25},
urldate = {2023-07-25},
journal = {BMC Health Services Research},
volume = {23},
number = {798},
abstract = {Background
Artificial Intelligence (AI) is recognized by emergency physicians (EPs) as an important technology that will affect clinical practice. Several AI-tools have already been developed to aid care delivery in emergency medicine (EM). However, many EM tools appear to have been developed without a cross-disciplinary needs assessment, making it difficult to understand their broader importance to general-practice. Clinician surveys about AI tools have been conducted within other medical specialties to help guide future design. This study aims to understand the needs of Canadian EPs for the apt use of AI-based tools.
Methods
A national cross-sectional, two-stage, mixed-method electronic survey of Canadian EPs was conducted from January-May 2022. The survey includes demographic and physician practice-pattern data, clinicians’ current use and perceptions of AI, and individual rankings of which EM work-activities most benefit from AI.
Results
The primary outcome is a ranked list of high-priority AI-tools for EM that physicians want translated into general use within the next 10 years. When ranking specific AI examples, ‘automated charting/report generation’, ‘clinical prediction rules’ and ‘monitoring vitals with early-warning detection’ were the top items. When ranking by physician work-activities, ‘AI-tools for documentation’, ‘AI-tools for computer use’ and ‘AI-tools for triaging patients’ were the top items. For secondary outcomes, EPs indicated AI was ‘likely’ (43.1%) or ‘extremely likely’ (43.7%) to be able to complete the task of ‘documentation’ and indicated either ‘a-great-deal’ (32.8%) or ‘quite-a-bit’ (39.7%) of potential for AI in EM. Further, EPs were either ‘strongly’ (48.5%) or ‘somewhat’ (39.8%) interested in AI for EM.
Conclusions
Physician input on the design of AI is essential to ensure the uptake of this technology. Translation of AI-tools to facilitate documentation is considered a high-priority, and respondents had high confidence that AI could facilitate this task. This study will guide future directions regarding the use of AI for EM and help direct efforts to address prevailing technology-translation barriers such as access to high-quality application-specific data and developing reporting guidelines for specific AI-applications. With a prioritized list of high-need AI applications, decision-makers can develop focused strategies to address these larger obstacles.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background
Artificial Intelligence (AI) is recognized by emergency physicians (EPs) as an important technology that will affect clinical practice. Several AI-tools have already been developed to aid care delivery in emergency medicine (EM). However, many EM tools appear to have been developed without a cross-disciplinary needs assessment, making it difficult to understand their broader importance to general-practice. Clinician surveys about AI tools have been conducted within other medical specialties to help guide future design. This study aims to understand the needs of Canadian EPs for the apt use of AI-based tools.
Methods
A national cross-sectional, two-stage, mixed-method electronic survey of Canadian EPs was conducted from January-May 2022. The survey includes demographic and physician practice-pattern data, clinicians’ current use and perceptions of AI, and individual rankings of which EM work-activities most benefit from AI.
Results
The primary outcome is a ranked list of high-priority AI-tools for EM that physicians want translated into general use within the next 10 years. When ranking specific AI examples, ‘automated charting/report generation’, ‘clinical prediction rules’ and ‘monitoring vitals with early-warning detection’ were the top items. When ranking by physician work-activities, ‘AI-tools for documentation’, ‘AI-tools for computer use’ and ‘AI-tools for triaging patients’ were the top items. For secondary outcomes, EPs indicated AI was ‘likely’ (43.1%) or ‘extremely likely’ (43.7%) to be able to complete the task of ‘documentation’ and indicated either ‘a-great-deal’ (32.8%) or ‘quite-a-bit’ (39.7%) of potential for AI in EM. Further, EPs were either ‘strongly’ (48.5%) or ‘somewhat’ (39.8%) interested in AI for EM.
Conclusions
Physician input on the design of AI is essential to ensure the uptake of this technology. Translation of AI-tools to facilitate documentation is considered a high-priority, and respondents had high confidence that AI could facilitate this task. This study will guide future directions regarding the use of AI for EM and help direct efforts to address prevailing technology-translation barriers such as access to high-quality application-specific data and developing reporting guidelines for specific AI-applications. With a prioritized list of high-need AI applications, decision-makers can develop focused strategies to address these larger obstacles.
Artificial Intelligence (AI) is recognized by emergency physicians (EPs) as an important technology that will affect clinical practice. Several AI-tools have already been developed to aid care delivery in emergency medicine (EM). However, many EM tools appear to have been developed without a cross-disciplinary needs assessment, making it difficult to understand their broader importance to general-practice. Clinician surveys about AI tools have been conducted within other medical specialties to help guide future design. This study aims to understand the needs of Canadian EPs for the apt use of AI-based tools.
Methods
A national cross-sectional, two-stage, mixed-method electronic survey of Canadian EPs was conducted from January-May 2022. The survey includes demographic and physician practice-pattern data, clinicians’ current use and perceptions of AI, and individual rankings of which EM work-activities most benefit from AI.
Results
The primary outcome is a ranked list of high-priority AI-tools for EM that physicians want translated into general use within the next 10 years. When ranking specific AI examples, ‘automated charting/report generation’, ‘clinical prediction rules’ and ‘monitoring vitals with early-warning detection’ were the top items. When ranking by physician work-activities, ‘AI-tools for documentation’, ‘AI-tools for computer use’ and ‘AI-tools for triaging patients’ were the top items. For secondary outcomes, EPs indicated AI was ‘likely’ (43.1%) or ‘extremely likely’ (43.7%) to be able to complete the task of ‘documentation’ and indicated either ‘a-great-deal’ (32.8%) or ‘quite-a-bit’ (39.7%) of potential for AI in EM. Further, EPs were either ‘strongly’ (48.5%) or ‘somewhat’ (39.8%) interested in AI for EM.
Conclusions
Physician input on the design of AI is essential to ensure the uptake of this technology. Translation of AI-tools to facilitate documentation is considered a high-priority, and respondents had high confidence that AI could facilitate this task. This study will guide future directions regarding the use of AI for EM and help direct efforts to address prevailing technology-translation barriers such as access to high-quality application-specific data and developing reporting guidelines for specific AI-applications. With a prioritized list of high-need AI applications, decision-makers can develop focused strategies to address these larger obstacles.
Samina Abidi; Tracey Rickards; William Van Woensel; Syed Sibte Raza Abidi
Digital Therapeutics for COPD Patient Self-Management: Needs Analysis and Design Study Proceedings Article
In: 19th World Congress on Medical and Health Informatics (MEDINFO 2023), 8–12 July 2023, Sydney, Australia, 2023.
BibTeX | Tags:
@inproceedings{nokey,
title = {Digital Therapeutics for COPD Patient Self-Management: Needs Analysis and Design Study},
author = {Samina Abidi and Tracey Rickards and William Van Woensel and Syed Sibte Raza Abidi},
year = {2023},
date = {2023-07-12},
urldate = {2023-00-00},
booktitle = {19th World Congress on Medical and Health Informatics (MEDINFO 2023), 8–12 July 2023, Sydney, Australia},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
William Van Woensel; Samina Abidi; Syed Sibte Raza Abidi
Decentralized Web-based Clinical Decision Support using Semantic GLEAN Workflows Proceedings Article
In: 21th International Conference on Artificial Intelligence in Medicine (AIME 2023), June 12-15, 2023, Portoroz, Slovenia, 2023.
BibTeX | Tags:
@inproceedings{nokey,
title = {Decentralized Web-based Clinical Decision Support using Semantic GLEAN Workflows},
author = {William Van Woensel and Samina Abidi and Syed Sibte Raza Abidi},
year = {2023},
date = {2023-06-05},
urldate = {2023-00-00},
booktitle = {21th International Conference on Artificial Intelligence in Medicine (AIME 2023), June 12-15, 2023, Portoroz, Slovenia},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
William Van Woensel; Samson W. Tu; Wojtek Michalowski; Syed Sibte Raza Abidi; Samina Abidi; Jose-Ramon Alonso; Alessio Bottrighi; Marc Carrier; Ruth Edry; Irit Hochberg; Malvika Rao; Stephen Kingwell; Alexandra Kogan; Mar Marcos; Begoña Martínez Salvador; Martin Michalowski; Luca Piovesan; David Riaño; Paolo Terenziani; Szymon Wilk; Mor Peleg
In: Journal of Biomedical Informatics, vol. 142, no. 104395, 2023, ISSN: 1532-0464.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {A Community-of-Practice-based Evaluation Methodology for Knowledge Intensive Computational Methods and its Application to Multimorbidity Decision Support},
author = {William Van Woensel and Samson W. Tu and Wojtek Michalowski and Syed Sibte Raza Abidi and Samina Abidi and Jose-Ramon Alonso and Alessio Bottrighi and Marc Carrier and Ruth Edry and Irit Hochberg and Malvika Rao and Stephen Kingwell and Alexandra Kogan and Mar Marcos and Begoña Martínez Salvador and Martin Michalowski and Luca Piovesan and David Riaño and Paolo Terenziani and Szymon Wilk and Mor Peleg},
url = {https://www.sciencedirect.com/science/article/pii/S1532046423001168},
doi = {https://doi.org/10.1016/j.jbi.2023.104395},
issn = {1532-0464},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
journal = {Journal of Biomedical Informatics},
volume = {142},
number = {104395},
abstract = {Objective
The study has dual objectives. Our first objective (1) is to develop a community-of-practice-based evaluation methodology for knowledge-intensive computational methods. We target a whitebox analysis of the computational methods to gain insight on their functional features and inner workings. In more detail, we aim to answer evaluation questions on (i) support offered by computational methods for functional features within the application domain; and (ii) in-depth characterizations of the underlying computational processes, models, data and knowledge of the computational methods. Our second objective (2) involves applying the evaluation methodology to answer questions (i) and (ii) for knowledge-intensive clinical decision support (CDS) methods, which operationalize clinical knowledge as computer interpretable guidelines (CIG); we focus on multimorbidity CIG-based clinical decision support (MGCDS) methods that target multimorbidity treatment plans.
Materials and Methods
Our methodology directly involves the research community of practice in (a) identifying functional features within the application domain; (b) defining exemplar case studies covering these features; and (c) solving the case studies using their developed computational methods—research groups detail their solutions and functional feature support in solution reports. Next, the study authors (d) perform a qualitative analysis of the solution reports, identifying and characterizing common themes (or dimensions) among the computational methods. This methodology is well suited to perform whitebox analysis, as it directly involves the respective developers in studying inner workings and feature support of computational methods. Moreover, the established evaluation parameters (e.g., features, case studies, themes) constitute a re-usable benchmark framework, which can be used to evaluate new computational methods as they are developed. We applied our community-of-practice-based evaluation methodology on MGCDS methods.
Results
Six research groups submitted comprehensive solution reports for the exemplar case studies. Solutions for two of these case studies were reported by all groups. We identified four evaluation dimensions: detection of adverse interactions, management strategy representation, implementation paradigms, and human-in-the-loop support.Based on our whitebox analysis, we present answers to the evaluation questions (i) and (ii) for MGCDS methods.
Discussion
The proposed evaluation methodology includes features of illuminative and comparison-based approaches; focusing on understanding rather than judging/scoring or identifying gaps in current methods. It involves answering evaluation questions with direct involvement of the research community of practice, who participate in setting up evaluation parameters and solving exemplar case studies. Our methodology was successfully applied to evaluate six MGCDS knowledge-intensive computational methods. We established that, while the evaluated methods provide a multifaceted set of solutions with different benefits and drawbacks, no single MGCDS method currently provides a comprehensive solution for MGCDS.
Conclusion
We posit that our evaluation methodology, applied here to gain new insights into MGCDS, can be used to assess other types of knowledge-intensive computational methods and answer other types of evaluation questions. Our case studies can be accessed at our GitHub repository (https://github.com/william-vw/MGCDS).
Keywords: Evaluation Study; Benchmarking; Multimorbidity; Decision Support Systems, Clinical; Computer-interpretable Clinical Guidelines},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objective
The study has dual objectives. Our first objective (1) is to develop a community-of-practice-based evaluation methodology for knowledge-intensive computational methods. We target a whitebox analysis of the computational methods to gain insight on their functional features and inner workings. In more detail, we aim to answer evaluation questions on (i) support offered by computational methods for functional features within the application domain; and (ii) in-depth characterizations of the underlying computational processes, models, data and knowledge of the computational methods. Our second objective (2) involves applying the evaluation methodology to answer questions (i) and (ii) for knowledge-intensive clinical decision support (CDS) methods, which operationalize clinical knowledge as computer interpretable guidelines (CIG); we focus on multimorbidity CIG-based clinical decision support (MGCDS) methods that target multimorbidity treatment plans.
Materials and Methods
Our methodology directly involves the research community of practice in (a) identifying functional features within the application domain; (b) defining exemplar case studies covering these features; and (c) solving the case studies using their developed computational methods—research groups detail their solutions and functional feature support in solution reports. Next, the study authors (d) perform a qualitative analysis of the solution reports, identifying and characterizing common themes (or dimensions) among the computational methods. This methodology is well suited to perform whitebox analysis, as it directly involves the respective developers in studying inner workings and feature support of computational methods. Moreover, the established evaluation parameters (e.g., features, case studies, themes) constitute a re-usable benchmark framework, which can be used to evaluate new computational methods as they are developed. We applied our community-of-practice-based evaluation methodology on MGCDS methods.
Results
Six research groups submitted comprehensive solution reports for the exemplar case studies. Solutions for two of these case studies were reported by all groups. We identified four evaluation dimensions: detection of adverse interactions, management strategy representation, implementation paradigms, and human-in-the-loop support.Based on our whitebox analysis, we present answers to the evaluation questions (i) and (ii) for MGCDS methods.
Discussion
The proposed evaluation methodology includes features of illuminative and comparison-based approaches; focusing on understanding rather than judging/scoring or identifying gaps in current methods. It involves answering evaluation questions with direct involvement of the research community of practice, who participate in setting up evaluation parameters and solving exemplar case studies. Our methodology was successfully applied to evaluate six MGCDS knowledge-intensive computational methods. We established that, while the evaluated methods provide a multifaceted set of solutions with different benefits and drawbacks, no single MGCDS method currently provides a comprehensive solution for MGCDS.
Conclusion
We posit that our evaluation methodology, applied here to gain new insights into MGCDS, can be used to assess other types of knowledge-intensive computational methods and answer other types of evaluation questions. Our case studies can be accessed at our GitHub repository (https://github.com/william-vw/MGCDS).
Keywords: Evaluation Study; Benchmarking; Multimorbidity; Decision Support Systems, Clinical; Computer-interpretable Clinical Guidelines
The study has dual objectives. Our first objective (1) is to develop a community-of-practice-based evaluation methodology for knowledge-intensive computational methods. We target a whitebox analysis of the computational methods to gain insight on their functional features and inner workings. In more detail, we aim to answer evaluation questions on (i) support offered by computational methods for functional features within the application domain; and (ii) in-depth characterizations of the underlying computational processes, models, data and knowledge of the computational methods. Our second objective (2) involves applying the evaluation methodology to answer questions (i) and (ii) for knowledge-intensive clinical decision support (CDS) methods, which operationalize clinical knowledge as computer interpretable guidelines (CIG); we focus on multimorbidity CIG-based clinical decision support (MGCDS) methods that target multimorbidity treatment plans.
Materials and Methods
Our methodology directly involves the research community of practice in (a) identifying functional features within the application domain; (b) defining exemplar case studies covering these features; and (c) solving the case studies using their developed computational methods—research groups detail their solutions and functional feature support in solution reports. Next, the study authors (d) perform a qualitative analysis of the solution reports, identifying and characterizing common themes (or dimensions) among the computational methods. This methodology is well suited to perform whitebox analysis, as it directly involves the respective developers in studying inner workings and feature support of computational methods. Moreover, the established evaluation parameters (e.g., features, case studies, themes) constitute a re-usable benchmark framework, which can be used to evaluate new computational methods as they are developed. We applied our community-of-practice-based evaluation methodology on MGCDS methods.
Results
Six research groups submitted comprehensive solution reports for the exemplar case studies. Solutions for two of these case studies were reported by all groups. We identified four evaluation dimensions: detection of adverse interactions, management strategy representation, implementation paradigms, and human-in-the-loop support.Based on our whitebox analysis, we present answers to the evaluation questions (i) and (ii) for MGCDS methods.
Discussion
The proposed evaluation methodology includes features of illuminative and comparison-based approaches; focusing on understanding rather than judging/scoring or identifying gaps in current methods. It involves answering evaluation questions with direct involvement of the research community of practice, who participate in setting up evaluation parameters and solving exemplar case studies. Our methodology was successfully applied to evaluate six MGCDS knowledge-intensive computational methods. We established that, while the evaluated methods provide a multifaceted set of solutions with different benefits and drawbacks, no single MGCDS method currently provides a comprehensive solution for MGCDS.
Conclusion
We posit that our evaluation methodology, applied here to gain new insights into MGCDS, can be used to assess other types of knowledge-intensive computational methods and answer other types of evaluation questions. Our case studies can be accessed at our GitHub repository (https://github.com/william-vw/MGCDS).
Keywords: Evaluation Study; Benchmarking; Multimorbidity; Decision Support Systems, Clinical; Computer-interpretable Clinical Guidelines
Syed Hani Raza Abidi; Nur Zincir-Heywood; Syed Sibte Raza Abidi; Kranthi Jalakam; Samina Abidi; L. Gunaratnam; R. Suri; H. Cardinale; A. Vinson; B. Prasad; M. Walsh; S. Yohanna; George Worthen; Karthik Tennankore
Characterizing Cluster-Based Frailty Phenotypes in a Multicenter Prospective Cohort of Kidney Transplant Candidates Proceedings Article
In: 19th World Congress on Medical and Health Informatics (MEDINFO 2023), 8–12 July 2023, Sydney, Australia, 2023.
BibTeX | Tags:
@inproceedings{nokey,
title = {Characterizing Cluster-Based Frailty Phenotypes in a Multicenter Prospective Cohort of Kidney Transplant Candidates},
author = {Syed Hani Raza Abidi and Nur Zincir-Heywood and Syed Sibte Raza Abidi and Kranthi Jalakam and Samina Abidi and L. Gunaratnam and R. Suri and H. Cardinale and A. Vinson and B. Prasad and M. Walsh and S. Yohanna and George Worthen and Karthik Tennankore},
year = {2023},
date = {2023-00-00},
urldate = {2023-00-00},
booktitle = {19th World Congress on Medical and Health Informatics (MEDINFO 2023), 8–12 July 2023, Sydney, Australia},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2022
William Van Woensel; Samina Abidi; Karthik Tennankore; George Worthen; Syed Sibte Raza Abidi
Explainable Decision Support using Task Network Models in Notation3: Computerizing Lipid Management Clinical Guidelines as Interactive Task Networks Proceedings Article
In: 20th International Conference on Artificial Intelligence in Medicine (AIME 2022), June 14-17, 2022, Halifax, Springer, 2022.
BibTeX | Tags: Clinical Decision Support Systems, Clinical guidelines, Notation3, Semantic Web reasoning, Task Network Models
@inproceedings{glean_2022,
title = {Explainable Decision Support using Task Network Models in Notation3: Computerizing Lipid Management Clinical Guidelines as Interactive Task Networks},
author = {William Van Woensel and Samina Abidi and Karthik Tennankore and George Worthen and Syed Sibte Raza Abidi},
year = {2022},
date = {2022-06-17},
urldate = {2022-06-17},
booktitle = {20th International Conference on Artificial Intelligence in Medicine (AIME 2022), June 14-17, 2022, Halifax},
publisher = {Springer},
keywords = {Clinical Decision Support Systems, Clinical guidelines, Notation3, Semantic Web reasoning, Task Network Models},
pubstate = {published},
tppubtype = {inproceedings}
}
William Van Woensel; Samina Abidi; Karthik Tennankore; George Worthen; Syed Sibte Raza Abidi
Clinical Guidelines as Executable and Interactive Workflows with FHIR-Compliant Health Data Input using GLEAN Proceedings Article
In: 20th International Conference on Artificial Intelligence in Medicine (AIME 2022): Demo Track, June 14-17, Halifax, 2022, Springer, 2022.
BibTeX | Tags: Clinical Decision Support Systems, Clinical guidelines, Notation3, Semantic Web reasoning, Task Network Models
@inproceedings{glean-demo,
title = {Clinical Guidelines as Executable and Interactive Workflows with FHIR-Compliant Health Data Input using GLEAN},
author = {William Van Woensel and Samina Abidi and Karthik Tennankore and George Worthen and Syed Sibte Raza Abidi},
year = {2022},
date = {2022-06-17},
urldate = {2022-06-17},
booktitle = {20th International Conference on Artificial Intelligence in Medicine (AIME 2022): Demo Track, June 14-17, Halifax, 2022},
publisher = {Springer},
keywords = {Clinical Decision Support Systems, Clinical guidelines, Notation3, Semantic Web reasoning, Task Network Models},
pubstate = {published},
tppubtype = {inproceedings}
}
Ali Daowd; Samina Abidi; Syed Sibte Raza Abidi
Knowledge Graph Completion Method Applied to Literature-Based Discovery for Predicting Missing Links Targeting Cancer Drug Repurposing Proceedings Article
In: 20th International Conference on Artificial Intelligence in Medicine (AIME 2022), June 14-17, 2022, Halifax, Springer, 2022.
BibTeX | Tags: Knowledge Graphs, Link Prediction, Literature-Based Discovery
@inproceedings{nokey,
title = {Knowledge Graph Completion Method Applied to Literature-Based Discovery for Predicting Missing Links Targeting Cancer Drug Repurposing},
author = {Ali Daowd and Samina Abidi and Syed Sibte Raza Abidi},
year = {2022},
date = {2022-06-14},
urldate = {2022-06-14},
booktitle = {20th International Conference on Artificial Intelligence in Medicine (AIME 2022), June 14-17, 2022, Halifax},
publisher = {Springer},
keywords = {Knowledge Graphs, Link Prediction, Literature-Based Discovery},
pubstate = {published},
tppubtype = {inproceedings}
}
William Van Woensel; Brett Taylor; Syed Sibte Raza Abidi
Towards an Adaptive Clinical Transcription System for In-Situ Transcribing of Patient Encounter Information Proceedings Article
In: Studies in Health Technology and Informatics, pp. 158–162, 2022, ISSN: 1879-8365.
Abstract | Links | BibTeX | Tags: Dictaphone, Machine Learning
@inproceedings{pmid35672991,
title = {Towards an Adaptive Clinical Transcription System for In-Situ Transcribing of Patient Encounter Information},
author = {William Van Woensel and Brett Taylor and Syed Sibte Raza Abidi},
doi = {10.3233/SHTI220052},
issn = {1879-8365},
year = {2022},
date = {2022-06-01},
urldate = {2022-06-01},
booktitle = {Studies in Health Technology and Informatics},
journal = {Stud Health Technol Inform},
volume = {290},
pages = {158--162},
abstract = {Electronic patient charts are essential for follow-up and multi-disciplinary care, but either take up an exorbitant amount of time during the patient encounter using a key-stroke entry system, or suffer from poor recall when made long after the encounter. Transcribing in-situ, natural dictations by the clinician, recorded during the encounter, with minimal workflow impact, is a promising solution. However, human transcription requires significant manual resources, whereas automated transcription currently lacks the accuracy for specialized clinical language. Our ultimate goal is to automate clinical transcription, particularly for Emergency Departments, with as an end-result a structured SOAP report. Towards this goal, we present the Adaptive Clinical Transcription System (ACTS). We compare the accuracy and processing times of state-of-the-art speech recognition tools, studying the feasibility of streaming-style dynamic transcription and opportunities of incremental learning.},
keywords = {Dictaphone, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Electronic patient charts are essential for follow-up and multi-disciplinary care, but either take up an exorbitant amount of time during the patient encounter using a key-stroke entry system, or suffer from poor recall when made long after the encounter. Transcribing in-situ, natural dictations by the clinician, recorded during the encounter, with minimal workflow impact, is a promising solution. However, human transcription requires significant manual resources, whereas automated transcription currently lacks the accuracy for specialized clinical language. Our ultimate goal is to automate clinical transcription, particularly for Emergency Departments, with as an end-result a structured SOAP report. Towards this goal, we present the Adaptive Clinical Transcription System (ACTS). We compare the accuracy and processing times of state-of-the-art speech recognition tools, studying the feasibility of streaming-style dynamic transcription and opportunities of incremental learning.
Benjamin Rose-Davis; William Van Woensel; Samina Raza Abidi; Elizabeth Stringer; Syed Sibte Raza Abidi
In: International Journal of Medical Informatics, 2022.
Links | BibTeX | Tags: Argument Theory, Knowledge Graphs, Patient Education, Semantic Web
@article{davis2022,
title = {Semantic Knowledge Modeling and Evaluation of Argument Theory to Develop Dialogue based Patient Education Systems for Chronic Disease Self-Management},
author = {Benjamin Rose-Davis and William Van Woensel and Samina Raza Abidi and Elizabeth Stringer and Syed Sibte Raza Abidi},
url = {https://www.sciencedirect.com/science/article/abs/pii/S1386505622000077},
year = {2022},
date = {2022-01-19},
urldate = {2022-01-18},
journal = {International Journal of Medical Informatics},
keywords = {Argument Theory, Knowledge Graphs, Patient Education, Semantic Web},
pubstate = {published},
tppubtype = {article}
}
2021
D O'Sullivan; W Van Woensel; S Wilk; S W Tu; W Michalowski; S Abidi; M Carrier; R Edry; I Hochberg; S Kingwell; K Kogan; M Michalowski; H O'Sullivan; M Peleg
Towards a framework for comparing functionalities of multimorbidity clinical decision support: A literature-based feature set and benchmark cases Proceedings Article
In: AMIA 2021 Annual Symposium, San Diego, CA, 2021.
BibTeX | Tags: Clinical Decision Support Systems, Comorbidity
@inproceedings{OSullivan2021,
title = {Towards a framework for comparing functionalities of multimorbidity clinical decision support: A literature-based feature set and benchmark cases},
author = {D O'Sullivan and W Van Woensel and S Wilk and S W Tu and W Michalowski and S Abidi and M Carrier and R Edry and I Hochberg and S Kingwell and K Kogan and M Michalowski and H O'Sullivan and M Peleg},
year = {2021},
date = {2021-10-30},
urldate = {2021-01-01},
booktitle = {AMIA 2021 Annual Symposium},
address = {San Diego, CA},
keywords = {Clinical Decision Support Systems, Comorbidity},
pubstate = {published},
tppubtype = {inproceedings}
}
William Van Woensel; Samina Abidi; Syed Sibte Raza Abidi
Towards Model-Driven Semantic Interfaces for Electronic Health Records on Multiple Platforms Using Notation3 Proceedings Article
In: 4th International Workshop on Semantic Web Meets Health Data Management (SWH’21) co-located with 20th International Semantic Web Conference (ISWC’21), 2021.
Links | BibTeX | Tags: Electronic Medical Record, Model-driven Engineering, Semantic Web
@inproceedings{wvw_swh_21,
title = {Towards Model-Driven Semantic Interfaces for Electronic Health Records on Multiple Platforms Using Notation3},
author = {William Van Woensel and Samina Abidi and Syed Sibte Raza Abidi},
url = {http://ceur-ws.org/Vol-3055/paper4.pdf},
year = {2021},
date = {2021-10-24},
urldate = {2021-10-24},
booktitle = {4th International Workshop on Semantic Web Meets Health Data Management (SWH’21) co-located with 20th International Semantic Web Conference (ISWC’21)},
keywords = {Electronic Medical Record, Model-driven Engineering, Semantic Web},
pubstate = {published},
tppubtype = {inproceedings}
}
Ali Daowd; Michael Barrett; Samina Abidi; Syed Sibte Raza Abidi
A Framework To Build A Causal Knowledge Graph for Chronic Diseases and Cancers By Discovering Semantic Associations from Biomedical Literature Proceedings Article
In: 2021 IEEE International Conference on Healthcare Informatics (ICHI), Victoria, BC, Canada, IEEE, 2021, ISBN: 978-1-6654-0132-6.
Links | BibTeX | Tags: Causal relations, Chronic Illness, Knowledge Graphs
@inproceedings{daowd_ichi_21,
title = {A Framework To Build A Causal Knowledge Graph for Chronic Diseases and Cancers By Discovering Semantic Associations from Biomedical Literature},
author = {Ali Daowd and Michael Barrett and Samina Abidi and Syed Sibte Raza Abidi},
doi = {10.1109/ICHI52183.2021.00016},
isbn = {978-1-6654-0132-6},
year = {2021},
date = {2021-08-09},
urldate = {2021-08-09},
booktitle = {2021 IEEE International Conference on Healthcare Informatics (ICHI), Victoria, BC, Canada},
publisher = {IEEE},
keywords = {Causal relations, Chronic Illness, Knowledge Graphs},
pubstate = {published},
tppubtype = {inproceedings}
}
William Van Woensel; Syed Sibte Raza Abidi; Samina Raza Abidi
In: Artificial Intelligence in Medicine, vol. 118, pp. 102127, 2021, ISSN: 0933-3657.
Abstract | Links | BibTeX | Tags: Clinical guidelines, Comorbidity, Decision Support Systems
@article{VANWOENSEL2021102127,
title = {Decision support for comorbid conditions via execution-time integration of clinical guidelines using transaction-based semantics and temporal planning},
author = {William Van Woensel and Syed Sibte Raza Abidi and Samina Raza Abidi},
url = {https://www.sciencedirect.com/science/article/pii/S0933365721001202},
doi = {https://doi.org/10.1016/j.artmed.2021.102127},
issn = {0933-3657},
year = {2021},
date = {2021-08-01},
journal = {Artificial Intelligence in Medicine},
volume = {118},
pages = {102127},
abstract = {In case of comorbidity, i.e., multiple medical conditions, Clinical Decision Support Systems (CDSS) should issue recommendations based on all relevant disease-related Clinical Practice Guidelines (CPG). However, treatments from multiple comorbid CPG often interact adversely (e.g., drug-drug interactions) or introduce operational inefficiencies (e.g., redundant scans). A common solution is the a-priori integration of computerized CPG, which involves integration decisions such as discarding, replacing or delaying clinical tasks (e.g., treatments) to avoid adverse interactions or inefficiencies. We argue this insufficiently deals with execution-time events: as the patient's health profile evolves, acute conditions occur, and real-time delays take place, new CPG integration decisions will often be needed, and prior ones may need to be reverted or undone. Any realistic CPG integration effort needs to further consider temporal aspects of clinical tasks—these are not only restricted by temporal constraints from CPGs (e.g., sequential relations, task durations) but also by CPG integration efforts (e.g., avoid treatment overlap). This poses a complex execution-time challenge and makes it difficult to determine an up-to-date, optimal comorbid care plan. We present a solution for dynamic integration of CPG in response to evolving health profiles and execution-time events. CPG integration policies are formulated by clinical experts for coping with comorbidity at execution-time, with clearly defined integration semantics that build on Description and Transaction Logics. A dynamic planning approach reconciles temporal constraints of CPG tasks at execution-time based on their importance, and continuously updates an optimal task schedule.},
keywords = {Clinical guidelines, Comorbidity, Decision Support Systems},
pubstate = {published},
tppubtype = {article}
}
In case of comorbidity, i.e., multiple medical conditions, Clinical Decision Support Systems (CDSS) should issue recommendations based on all relevant disease-related Clinical Practice Guidelines (CPG). However, treatments from multiple comorbid CPG often interact adversely (e.g., drug-drug interactions) or introduce operational inefficiencies (e.g., redundant scans). A common solution is the a-priori integration of computerized CPG, which involves integration decisions such as discarding, replacing or delaying clinical tasks (e.g., treatments) to avoid adverse interactions or inefficiencies. We argue this insufficiently deals with execution-time events: as the patient's health profile evolves, acute conditions occur, and real-time delays take place, new CPG integration decisions will often be needed, and prior ones may need to be reverted or undone. Any realistic CPG integration effort needs to further consider temporal aspects of clinical tasks—these are not only restricted by temporal constraints from CPGs (e.g., sequential relations, task durations) but also by CPG integration efforts (e.g., avoid treatment overlap). This poses a complex execution-time challenge and makes it difficult to determine an up-to-date, optimal comorbid care plan. We present a solution for dynamic integration of CPG in response to evolving health profiles and execution-time events. CPG integration policies are formulated by clinical experts for coping with comorbidity at execution-time, with clearly defined integration semantics that build on Description and Transaction Logics. A dynamic planning approach reconciles temporal constraints of CPG tasks at execution-time based on their importance, and continuously updates an optimal task schedule.
Jaber Rad; Jason Quinn; Calvino Cheng; Robert Liwski; Samina Raza Abidi; Syed Sibte Raza Abidi
Using Interactive Visual Analytics to Optimize in Real-Time Blood Products Inventory at a Blood Bank Proceedings Article
In: 31st Medical Informatics Europe (MIE2021), 2021.
BibTeX | Tags: Blood Inventory Management, Clinical Decision Support Systems, Visual analytics
@inproceedings{rad2021,
title = {Using Interactive Visual Analytics to Optimize in Real-Time Blood Products Inventory at a Blood Bank},
author = {Jaber Rad and Jason Quinn and Calvino Cheng and Robert Liwski and Samina Raza Abidi and Syed Sibte Raza Abidi},
year = {2021},
date = {2021-05-29},
urldate = {2021-05-29},
booktitle = {31st Medical Informatics Europe (MIE2021)},
keywords = {Blood Inventory Management, Clinical Decision Support Systems, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
Ali Daowd; Michael Barrett; Samina Raza Abidi; Syed Sibte Raza Abidi
Building a Knowledge Graph Representing Causal Associations between Risk Factors and Incidence of Breast Cancer Proceedings Article
In: 31st Medical Informatics Europe (MIE2021), 2021.
BibTeX | Tags: Knowledge Graphs, Risk Factors
@inproceedings{daowd2021,
title = {Building a Knowledge Graph Representing Causal Associations between Risk Factors and Incidence of Breast Cancer},
author = {Ali Daowd and Michael Barrett and Samina Raza Abidi and Syed Sibte Raza Abidi},
year = {2021},
date = {2021-05-29},
booktitle = {31st Medical Informatics Europe (MIE2021)},
keywords = {Knowledge Graphs, Risk Factors},
pubstate = {published},
tppubtype = {inproceedings}
}
Aditi Nair; Syed Sibte Raza Abidi; William Van Woensel; Samina Raza Abidi
Ontology-based Personalized Cognitive Behavioural Plans for Patients with Mild Depression Proceedings Article
In: 31st Medical Informatics Europe (MIE2021), pp. 729-733, 2021.
Links | BibTeX | Tags: Behavioural Change Theory, Ontology Engineering
@inproceedings{nair2021,
title = {Ontology-based Personalized Cognitive Behavioural Plans for Patients with Mild Depression},
author = {Aditi Nair and Syed Sibte Raza Abidi and William Van Woensel and Samina Raza Abidi},
url = {https://doi.org/10.3233/shti210268},
year = {2021},
date = {2021-05-29},
urldate = {2021-05-29},
booktitle = {31st Medical Informatics Europe (MIE2021)},
pages = {729-733},
keywords = {Behavioural Change Theory, Ontology Engineering},
pubstate = {published},
tppubtype = {inproceedings}
}
Michael Barrett; Ali Daowd; Samina Raza Abidi; Syed Sibte Raza
A Knowledge Graph of Mechanistic Associations Between COVID-19, Diabetes Mellitus and Kidney Diseases Proceedings Article
In: 31st Medical Informatics Europe (MIE2021), 2021.
BibTeX | Tags: CoVid-19, Knowledge Graphs
@inproceedings{barrett2021,
title = {A Knowledge Graph of Mechanistic Associations Between COVID-19, Diabetes Mellitus and Kidney Diseases},
author = {Michael Barrett and Ali Daowd and Samina Raza Abidi and Syed Sibte Raza},
year = {2021},
date = {2021-05-29},
booktitle = {31st Medical Informatics Europe (MIE2021)},
keywords = {CoVid-19, Knowledge Graphs},
pubstate = {published},
tppubtype = {inproceedings}
}
2020
William Van Woensel; Samina Abidi; Borna Jafarpour; Syed Sibte Raza Abidi
A CIG Integration Framework to Provide Decision Support for Comorbid Conditions using Transaction-based Semantics and Temporal Planning Proceedings Article
In: International Conference on Artificial Intelligence in Medicine (AIME 2020), 2020.
Abstract | BibTeX | Tags: Clinical Decision Support Systems, Clinical Practice Guidelines, Comorbidities, Ontology, Semantic Web
@inproceedings{VANWOENSEL2020-COCIG1,
title = {A CIG Integration Framework to Provide Decision Support for Comorbid Conditions using Transaction-based Semantics and Temporal Planning},
author = {William Van Woensel and Samina Abidi and Borna Jafarpour and Syed Sibte Raza Abidi},
year = {2020},
date = {2020-08-01},
urldate = {2020-08-01},
booktitle = {International Conference on Artificial Intelligence in Medicine (AIME 2020)},
abstract = {Managing comorbid conditions, i.e., patients with multiple medical conditions, is quite challenging for Clinical Decision Support Systems (CDSS) based on computerized Clinical Practice Guidelines (CPG). In case of comorbidity, CDSS will need to recommend treatments from multiple different CPG, which may adversely interact (e.g., drug-disease interactions), or introduce inefficiencies. A-priori, static integration of computerized comorbid CPG is insufficient for clinical practice. In this paper, we present a solution for dynamic integration of CPG in response to evolving health profiles. Using Description and Transaction Logics, we define a set of CIG integration semantics for encoding integration decisions that cope with comorbidity issues at execution-time. These dynamic, transaction-based semantics are well-suited to roll back prior decisions when no longer safe or efficient; or, inversely, apply new decisions when relevant. Moreover, comorbid CIG integration should consider temporal properties of CIG tasks—at execution-time, these properties will be influenced by a range of temporal constraints. Given all temporal constraints, optimal task schedules will be calculated that will determine the feasibility of CIG integration decisions.},
keywords = {Clinical Decision Support Systems, Clinical Practice Guidelines, Comorbidities, Ontology, Semantic Web},
pubstate = {published},
tppubtype = {inproceedings}
}
Managing comorbid conditions, i.e., patients with multiple medical conditions, is quite challenging for Clinical Decision Support Systems (CDSS) based on computerized Clinical Practice Guidelines (CPG). In case of comorbidity, CDSS will need to recommend treatments from multiple different CPG, which may adversely interact (e.g., drug-disease interactions), or introduce inefficiencies. A-priori, static integration of computerized comorbid CPG is insufficient for clinical practice. In this paper, we present a solution for dynamic integration of CPG in response to evolving health profiles. Using Description and Transaction Logics, we define a set of CIG integration semantics for encoding integration decisions that cope with comorbidity issues at execution-time. These dynamic, transaction-based semantics are well-suited to roll back prior decisions when no longer safe or efficient; or, inversely, apply new decisions when relevant. Moreover, comorbid CIG integration should consider temporal properties of CIG tasks—at execution-time, these properties will be influenced by a range of temporal constraints. Given all temporal constraints, optimal task schedules will be calculated that will determine the feasibility of CIG integration decisions.
Jaber Rad; Calvino Cheng; Jason G Quinn; Samina Abidi; Robert Liwski; Syed Sibte Raza Abidi
An AI-Driven Predictive Modelling Framework to Analyze and Visualize Blood Product Transactional Data for Reducing Blood Products’ Discards Proceedings Article
In: International Conference on Artificial Intelligence in Medicine (AIME 2020), 2020.
Abstract | BibTeX | Tags: Big Data, Blood Inventory Management, Blood product wastage, Data visualization, Machine Learning, Sequence prediction, Visual analytics
@inproceedings{JRAD2020,
title = {An AI-Driven Predictive Modelling Framework to Analyze and Visualize Blood Product Transactional Data for Reducing Blood Products’ Discards},
author = {Jaber Rad and Calvino Cheng and Jason G Quinn and Samina Abidi and Robert Liwski and Syed Sibte Raza Abidi},
year = {2020},
date = {2020-08-01},
urldate = {2020-08-01},
booktitle = {International Conference on Artificial Intelligence in Medicine (AIME 2020)},
abstract = {Maintaining an equilibrium between shortage and wastage in blood inventories is challenging due to the perishable nature of blood products. Re-search in blood product inventory management has predominantly focused on reducing wastage due to outdates (i.e. expiry of the blood product), whereas wast-age due to discards, related to the lifecycle of a blood product, is not well investigated. In this study, we investigate machine learning methods to analyze blood product transition sequences in a large real-life transactional dataset of Red Blood Cells (RBC) to predict potential blood product discard. Our prediction models are able to predict with 79% accuracy potential discards based on the blood product’s current transaction data. We applied advanced data visualizations methods to develop an interactive blood inventory dashboard to help laboratory managers to probe blood units’ lifecycles to identify discard causes.},
keywords = {Big Data, Blood Inventory Management, Blood product wastage, Data visualization, Machine Learning, Sequence prediction, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
Maintaining an equilibrium between shortage and wastage in blood inventories is challenging due to the perishable nature of blood products. Re-search in blood product inventory management has predominantly focused on reducing wastage due to outdates (i.e. expiry of the blood product), whereas wast-age due to discards, related to the lifecycle of a blood product, is not well investigated. In this study, we investigate machine learning methods to analyze blood product transition sequences in a large real-life transactional dataset of Red Blood Cells (RBC) to predict potential blood product discard. Our prediction models are able to predict with 79% accuracy potential discards based on the blood product’s current transaction data. We applied advanced data visualizations methods to develop an interactive blood inventory dashboard to help laboratory managers to probe blood units’ lifecycles to identify discard causes.
Eseohen Imoukhome; Lori Weeks; Samina Abidi
Fall Prevention and Management App Prototype for the Elderly and Their Caregivers: Design, Implementation, and Evaluation Journal Article
In: International Journal of Extreme Automation and Connectivity in Healthcare, vol. 2, pp. 48-67, 2020.
@article{article,
title = {Fall Prevention and Management App Prototype for the Elderly and Their Caregivers: Design, Implementation, and Evaluation},
author = {Eseohen Imoukhome and Lori Weeks and Samina Abidi},
doi = {10.4018/IJEACH.2020010104},
year = {2020},
date = {2020-01-01},
journal = {International Journal of Extreme Automation and Connectivity in Healthcare},
volume = {2},
pages = {48-67},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
William Van Woensel; Patrice C Roy; Syed Sibte Raza Abidi; Samina Raza Abidi
Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods Journal Article
In: Artificial Intelligence in Medicine, vol. 108, pp. 101931, 2020, ISSN: 0933-3657.
Abstract | Links | BibTeX | Tags: Activities of daily living, Ambient assisted living, Ambient Intelligence, Ambient sensors, Chronic disease self-management, Data fusion, eHealth Platform, Indoor Localization, Machine Learning, Self-Management, Semantic Web, Virtual care
@article{VANWOENSEL2020101931,
title = {Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods},
author = {William Van Woensel and Patrice C Roy and Syed Sibte Raza Abidi and Samina Raza Abidi},
url = {http://www.sciencedirect.com/science/article/pii/S0933365720301275
https://authors.elsevier.com/a/1bTwR3KEGaD3xR},
doi = {https://doi.org/10.1016/j.artmed.2020.101931},
issn = {0933-3657},
year = {2020},
date = {2020-01-01},
journal = {Artificial Intelligence in Medicine},
volume = {108},
pages = {101931},
abstract = {In a digitally enabled healthcare setting, we posit that an individual’s current location is pivotal for supporting many virtual care services—such as tailoring educational content towards an individual’s current location, and, hence, current stage in an acute care process; improving activity recognition for supporting self-management in a home-based setting; and guiding individuals with cognitive decline through daily activities in their home. However, unobtrusively estimating an individual’s indoor location in real-world care settings is still a challenging problem. Moreover, the needs of location-specific care interventions go beyond absolute coordinates and require the individual’s discrete semantic location; i.e., it is the concrete type of an individual’s location (e.g., exam vs. waiting room; bathroom vs. kitchen) that will drive the tailoring of educational content or recognition of activities. We utilized Machine Learning methods to accurately identify an individual’s discrete location, together with knowledge-based models and tools to supply the associated semantics of identified locations. We considered clustering solutions to improve localization accuracy at the expense of granularity; and investigate sensor fusion-based heuristics to rule out false location estimates. We present an AI-driven indoor localization approach that integrates both data-driven and knowledge-based processes and artifacts. We illustrate the application of our approach in two compelling healthcare use cases, and empirically validated our localization approach at the emergency unit of a large Canadian pediatric hospital.},
keywords = {Activities of daily living, Ambient assisted living, Ambient Intelligence, Ambient sensors, Chronic disease self-management, Data fusion, eHealth Platform, Indoor Localization, Machine Learning, Self-Management, Semantic Web, Virtual care},
pubstate = {published},
tppubtype = {article}
}
In a digitally enabled healthcare setting, we posit that an individual’s current location is pivotal for supporting many virtual care services—such as tailoring educational content towards an individual’s current location, and, hence, current stage in an acute care process; improving activity recognition for supporting self-management in a home-based setting; and guiding individuals with cognitive decline through daily activities in their home. However, unobtrusively estimating an individual’s indoor location in real-world care settings is still a challenging problem. Moreover, the needs of location-specific care interventions go beyond absolute coordinates and require the individual’s discrete semantic location; i.e., it is the concrete type of an individual’s location (e.g., exam vs. waiting room; bathroom vs. kitchen) that will drive the tailoring of educational content or recognition of activities. We utilized Machine Learning methods to accurately identify an individual’s discrete location, together with knowledge-based models and tools to supply the associated semantics of identified locations. We considered clustering solutions to improve localization accuracy at the expense of granularity; and investigate sensor fusion-based heuristics to rule out false location estimates. We present an AI-driven indoor localization approach that integrates both data-driven and knowledge-based processes and artifacts. We illustrate the application of our approach in two compelling healthcare use cases, and empirically validated our localization approach at the emergency unit of a large Canadian pediatric hospital.
2019
William Van Woensel; Samina Raza Abidi; Syed Sibte Raza Abidi
Pro-Actively Guiding Patients through ADL via Knowledge-Based and Context-Driven Activity Recognition Proceedings Article
In: 17th World Congress on Medical and Health Informatics (MEDINFO'19), Aug 26-30, pp. 863 - 867, IOS Press, Lyon, France, 2019.
@inproceedings{VanWoensel2019,
title = {Pro-Actively Guiding Patients through ADL via Knowledge-Based and Context-Driven Activity Recognition},
author = {William Van Woensel and Samina Raza Abidi and Syed Sibte Raza Abidi},
url = {http://ebooks.iospress.nl/publication/52111},
doi = {10.3233/SHTI190346},
year = {2019},
date = {2019-08-26},
booktitle = {17th World Congress on Medical and Health Informatics (MEDINFO'19), Aug 26-30},
volume = {264},
pages = {863 - 867},
publisher = {IOS Press},
address = {Lyon, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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}
}
Ali Daowd; Syed M Faizan; Samina Raza Abidi; Ashraf Abusharekh; A Shehzad; Syed Sibte Raza Abidi
Towards Personalized Lifetime Health: A Platform for Early Multimorbid Chronic Disease Risk Assessment and Mitigation Proceedings Article
In: 17th World Congress on Medical and Health Informatics (MEDINFO'19), Aug 26-30, pp. 935 - 939, Lyon, France, 2019.
@inproceedings{Daowd2019,
title = {Towards Personalized Lifetime Health: A Platform for Early Multimorbid Chronic Disease Risk Assessment and Mitigation},
author = {Ali Daowd and Syed M Faizan and Samina Raza Abidi and Ashraf Abusharekh and A Shehzad and Syed Sibte Raza Abidi},
url = {http://ebooks.iospress.nl/publication/52126},
doi = {10.3233/SHTI190361},
year = {2019},
date = {2019-08-26},
booktitle = {17th World Congress on Medical and Health Informatics (MEDINFO'19), Aug 26-30},
volume = {264},
pages = {935 - 939},
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}
}
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.
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.
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.
2018
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}
}
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}
}
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.
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},
tppubtype = {article}
}
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}
}
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}
}
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