About Me
I am an Assistant Professor in Health Informatics and Information Systems at the University of Ottawa. I also hold an Adjunct Faculty position at the Faculty of Computer Science at Dalhousie University. Before that, I was a Research Associate and Post-Doctoral Fellow at the NICHE Research Group at Dalhousie University. I was a teaching assistant for 6 years at the Vrije Universiteit Brussel, obtaining the degree of Doctor in Sciences in 2013.
Research Interests
My research interests lie at the crossroads of Knowledge Representation and Reasoning (KR), Information Systems (IS) engineering, and Mobile Computing. In particular, I am interested in applying these technologies to innovate domains such as healthcare, law, government and business.
Publications
2023
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
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}
}
William Van Woensel; Floriano Scioscia; Giuseppe Loseto; Oshani Seneviratne; Evan Patton; Samina Abidi; Lalana Kagal
Explainable Clinical Decision Support: Towards Patient-Facing Explanations for Education and Long-term Behavior Change Proceedings Article
In: 20th International Conference on Artificial Intelligence in Medicine (AIME 2022): Demo Track, June 14-17, Halifax, 2022, Springer, 2022.
BibTeX | Tags: Explainability, Semantic Web reasoning
@inproceedings{explain_2022,
title = {Explainable Clinical Decision Support: Towards Patient-Facing Explanations for Education and Long-term Behavior Change},
author = {William Van Woensel and Floriano Scioscia and Giuseppe Loseto and Oshani Seneviratne and Evan Patton and Samina Abidi and Lalana Kagal},
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 = {Explainability, Semantic Web reasoning},
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}
}
G Loseto; E Patton; O Seneviratne; W Van Woensel; F Scioscia; L Kagal
Mobile App Development for the Semantic Web of Things with Punya Proceedings Article
In: 20th International Semantic Web Conference: Demo Track (ISWC '21), 2021.
BibTeX | Tags: Internet of Things, Mobile Computing, Semantic Web
@inproceedings{Loseto2021,
title = {Mobile App Development for the Semantic Web of Things with Punya},
author = {G Loseto and E Patton and O Seneviratne and W Van Woensel and F Scioscia and L Kagal},
year = {2021},
date = {2021-10-24},
urldate = {2021-01-01},
booktitle = {20th International Semantic Web Conference: Demo Track (ISWC '21)},
keywords = {Internet of Things, Mobile Computing, Semantic Web},
pubstate = {published},
tppubtype = {inproceedings}
}
Oshani Seneviratne; William Van Woensel; Giuseppe Loseto; Floriano Scioscia; Evan Patton; Lalana Kagal
Rapid Prototyping of Mobile Apps for Clinical Research using Semantic Web Technologies Proceedings Article
In: 20th International Semantic Web Conference: Demo Track (ISWC '21), 2021.
BibTeX | Tags: Mobile Computing, Mobile Health, Semantic Web
@inproceedings{Seneviratne2021,
title = {Rapid Prototyping of Mobile Apps for Clinical Research using Semantic Web Technologies},
author = {Oshani Seneviratne and William Van Woensel and Giuseppe Loseto and Floriano Scioscia and Evan Patton and Lalana Kagal},
year = {2021},
date = {2021-10-24},
urldate = {2021-01-01},
booktitle = {20th International Semantic Web Conference: Demo Track (ISWC '21)},
keywords = {Mobile Computing, Mobile Health, Semantic Web},
pubstate = {published},
tppubtype = {inproceedings}
}
Evan Patton; William Van Woensel; Oshani Seneviratne; Giuseppe Loseto; Floriano Scioscia; Lalana Kagal
The Punya Platform: Building Mobile Research Apps with Linked Data and Semantic Features Proceedings Article
In: 20th International Semantic Web Conference (ISWC '21), 2021.
BibTeX | Tags: Mobile Computing, Mobile Health, Semantic Web
@inproceedings{Patton2021,
title = {The Punya Platform: Building Mobile Research Apps with Linked Data and Semantic Features},
author = {Evan Patton and William Van Woensel and Oshani Seneviratne and Giuseppe Loseto and Floriano Scioscia and Lalana Kagal},
year = {2021},
date = {2021-10-24},
urldate = {2021-01-01},
booktitle = {20th International Semantic Web Conference (ISWC '21)},
keywords = {Mobile Computing, Mobile Health, Semantic Web},
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}
}
William Van Woensel; Manal Elnenaei; Syed Sibte Raza Abidi; David B Clarke; Syed Ali Imran
Staged Reflexive Artificial Intelligence Driven Testing Algorithms for Early Diagnosis of Pituitary Disorders Journal Article
In: Clinical Biochemistry, 2021.
Links | BibTeX | Tags: Clinical Decision Support Systems, Health Informatics, Reflex Protocols
@article{VanWoensel2021b,
title = {Staged Reflexive Artificial Intelligence Driven Testing Algorithms for Early Diagnosis of Pituitary Disorders},
author = {William Van Woensel and Manal Elnenaei and Syed Sibte Raza Abidi and David B Clarke and Syed Ali Imran},
url = {https://www.sciencedirect.com/science/article/pii/S0009912021002265},
year = {2021},
date = {2021-08-20},
urldate = {2021-08-20},
journal = {Clinical Biochemistry},
keywords = {Clinical Decision Support Systems, Health Informatics, Reflex Protocols},
pubstate = {published},
tppubtype = {article}
}
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.
William Van Woensel; Manal Elnenaei; Syed Ali Imran; Syed Sibte Raza Abidi
Semantic Web Framework to Computerize Staged Reflex Testing Protocols to Mitigate Underutilization of Pathology Tests for Diagnosing Pituitary Disorders Proceedings Article
In: International Conference on Artificial Intelligence in Medicine (AIME), 2021.
BibTeX | Tags: Clinical Decision Support Systems, Reflex Protocols, Semantic Web
@inproceedings{wvw_aime_21,
title = {Semantic Web Framework to Computerize Staged Reflex Testing Protocols to Mitigate Underutilization of Pathology Tests for Diagnosing Pituitary Disorders},
author = {William Van Woensel and Manal Elnenaei and Syed Ali Imran and Syed Sibte Raza Abidi},
year = {2021},
date = {2021-06-16},
urldate = {2021-06-16},
booktitle = {International Conference on Artificial Intelligence in Medicine (AIME)},
keywords = {Clinical Decision Support Systems, Reflex Protocols, Semantic Web},
pubstate = {published},
tppubtype = {inproceedings}
}
William Van Woensel; Chad Armstrong; Malavan Rajaratnam; Vaibhav Gupta; Syed Sibte Raza Abidi
Using Knowledge Graphs to Plausibly Infer Missing Associations in EMR Data Proceedings Article
In: 31st Medical Informatics Europe (MIE2021), pp. 417-421, 2021.
Links | BibTeX | Tags: Clinical Decision Support Systems, Knowledge Graphs, Plausible reasoning, Semantic Similarity
@inproceedings{wvw_mie21_1,
title = {Using Knowledge Graphs to Plausibly Infer Missing Associations in EMR Data},
author = {William Van Woensel and Chad Armstrong and Malavan Rajaratnam and Vaibhav Gupta and Syed Sibte Raza Abidi},
url = {https://doi.org/10.3233/shti210192},
year = {2021},
date = {2021-05-29},
urldate = {2021-05-29},
booktitle = {31st Medical Informatics Europe (MIE2021)},
pages = {417-421},
keywords = {Clinical Decision Support Systems, Knowledge Graphs, Plausible reasoning, Semantic Similarity},
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}
}
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.
Ignacio Miralles; Carlos Granell; Laura Díaz-Sanahuja; William Van Woensel; Juana Bretón-López; Adriana Mira; Diana Castilla; Sven Casteleyn
Smartphone Apps for the Treatment of Mental Disorders: Systematic Review Journal Article
In: JMIR Mhealth Uhealth, vol. 8, no. 4, pp. e14897, 2020, ISSN: 2291-5222.
Abstract | Links | BibTeX | Tags: mental health; mental disorders; treatment; intervention; mHealth; smartphone; mobile phone; mobile apps; systematic review
@article{info:doi/10.2196/14897,
title = {Smartphone Apps for the Treatment of Mental Disorders: Systematic Review},
author = {Ignacio Miralles and Carlos Granell and Laura Díaz-Sanahuja and William Van Woensel and Juana Bretón-López and Adriana Mira and Diana Castilla and Sven Casteleyn},
url = {http://www.ncbi.nlm.nih.gov/pubmed/32238332},
doi = {10.2196/14897},
issn = {2291-5222},
year = {2020},
date = {2020-04-02},
journal = {JMIR Mhealth Uhealth},
volume = {8},
number = {4},
pages = {e14897},
abstract = {Background: Smartphone apps are an increasingly popular means for delivering psychological interventions to patients suffering from a mental disorder. In line with this popularity, there is a need to analyze and summarize the state of the art, both from a psychological and technical perspective. Objective: This study aimed to systematically review the literature on the use of smartphones for psychological interventions. Our systematic review has the following objectives: (1) analyze the coverage of mental disorders in research articles per year; (2) study the types of assessment in research articles per mental disorder per year; (3) map the use of advanced technical features, such as sensors, and novel software features, such as personalization and social media, per mental disorder; (4) provide an overview of smartphone apps per mental disorder; and (5) provide an overview of the key characteristics of empirical assessments with rigorous designs (ie, randomized controlled trials [RCTs]). Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for systematic reviews were followed. We performed searches in Scopus, Web of Science, American Psychological Association PsycNET, and Medical Literature Analysis and Retrieval System Online, covering a period of 6 years (2013-2018). We included papers that described the use of smartphone apps to deliver psychological interventions for known mental disorders. We formed multidisciplinary teams, comprising experts in psychology and computer science, to select and classify articles based on psychological and technical features. Results: We found 158 articles that met the inclusion criteria. We observed an increasing interest in smartphone-based interventions over time. Most research targeted disorders with high prevalence, that is, depressive (31/158,19.6%) and anxiety disorders (18/158, 11.4%). Of the total, 72.7% (115/158) of the papers focused on six mental disorders: depression, anxiety, trauma and stressor-related, substance-related and addiction, schizophrenia spectrum, and other psychotic disorders, or a combination of disorders. More than half of known mental disorders were not or very scarcely (<3%) represented. An increasing number of studies were dedicated to assessing clinical effects, but RCTs were still a minority (25/158, 15.8%). From a technical viewpoint, interventions were leveraging the improved modalities (screen and sound) and interactivity of smartphones but only sparingly leveraged their truly novel capabilities, such as sensors, alternative delivery paradigms, and analytical methods. Conclusions: There is a need for designing interventions for the full breadth of mental disorders, rather than primarily focusing on most prevalent disorders. We further contend that an increasingly systematic focus, that is, involving RCTs, is needed to improve the robustness and trustworthiness of assessments. Regarding technical aspects, we argue that further exploration and innovative use of the novel capabilities of smartphones are needed to fully realize their potential for the treatment of mental health disorders.},
keywords = {mental health; mental disorders; treatment; intervention; mHealth; smartphone; mobile phone; mobile apps; systematic review},
pubstate = {published},
tppubtype = {article}
}
Background: Smartphone apps are an increasingly popular means for delivering psychological interventions to patients suffering from a mental disorder. In line with this popularity, there is a need to analyze and summarize the state of the art, both from a psychological and technical perspective. Objective: This study aimed to systematically review the literature on the use of smartphones for psychological interventions. Our systematic review has the following objectives: (1) analyze the coverage of mental disorders in research articles per year; (2) study the types of assessment in research articles per mental disorder per year; (3) map the use of advanced technical features, such as sensors, and novel software features, such as personalization and social media, per mental disorder; (4) provide an overview of smartphone apps per mental disorder; and (5) provide an overview of the key characteristics of empirical assessments with rigorous designs (ie, randomized controlled trials [RCTs]). Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for systematic reviews were followed. We performed searches in Scopus, Web of Science, American Psychological Association PsycNET, and Medical Literature Analysis and Retrieval System Online, covering a period of 6 years (2013-2018). We included papers that described the use of smartphone apps to deliver psychological interventions for known mental disorders. We formed multidisciplinary teams, comprising experts in psychology and computer science, to select and classify articles based on psychological and technical features. Results: We found 158 articles that met the inclusion criteria. We observed an increasing interest in smartphone-based interventions over time. Most research targeted disorders with high prevalence, that is, depressive (31/158,19.6%) and anxiety disorders (18/158, 11.4%). Of the total, 72.7% (115/158) of the papers focused on six mental disorders: depression, anxiety, trauma and stressor-related, substance-related and addiction, schizophrenia spectrum, and other psychotic disorders, or a combination of disorders. More than half of known mental disorders were not or very scarcely (<3%) represented. An increasing number of studies were dedicated to assessing clinical effects, but RCTs were still a minority (25/158, 15.8%). From a technical viewpoint, interventions were leveraging the improved modalities (screen and sound) and interactivity of smartphones but only sparingly leveraged their truly novel capabilities, such as sensors, alternative delivery paradigms, and analytical methods. Conclusions: There is a need for designing interventions for the full breadth of mental disorders, rather than primarily focusing on most prevalent disorders. We further contend that an increasingly systematic focus, that is, involving RCTs, is needed to improve the robustness and trustworthiness of assessments. Regarding technical aspects, we argue that further exploration and innovative use of the novel capabilities of smartphones are needed to fully realize their potential for the treatment of mental health disorders.
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}
}
Patrice C. Roy; William Van Woensel; Andy Wilcox; Syed Sibte Raza Abidi
Mobile Indoor Localization with Bluetooth Beacons in a Pediatric Emergency Department Using Clustering, Rule-based Classification and High-level Heuristics Proceedings Article
In: 17th Conf. on Artificial Intelligence in Medicine (AIME2019), June 26-29, pp. 216–226, Springer International Publishing, Poznan, Poland, 2019, ISBN: 978-3-030-21642-9.
@inproceedings{Roy2019,
title = {Mobile Indoor Localization with Bluetooth Beacons in a Pediatric Emergency Department Using Clustering, Rule-based Classification and High-level Heuristics},
author = {Patrice C. Roy and William Van Woensel and Andy Wilcox and Syed Sibte Raza Abidi},
url = {https://link.springer.com/chapter/10.1007/978-3-030-21642-9_27},
doi = {10.1007/978-3-030-21642-9_27},
isbn = {978-3-030-21642-9},
year = {2019},
date = {2019-06-26},
booktitle = {17th Conf. on Artificial Intelligence in Medicine (AIME2019), June 26-29},
pages = {216--226},
publisher = {Springer International Publishing},
address = {Poznan, Poland},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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}
}
Borna Jafarpour; Samina Raza Abidi; William Van Woensel; Syed Sibte Raza Abidi
Execution-Time Integration of Clinical Practice Guidelines To Provide Decision Support for Comorbid Conditions Journal Article
In: Artificial Intelligence in Medicine, vol. 94, pp. 117-137, 2019, ISSN: 0933-3657.
Abstract | Links | BibTeX | Tags:
@article{JAFARPOUR2019,
title = {Execution-Time Integration of Clinical Practice Guidelines To Provide Decision Support for Comorbid Conditions},
author = {Borna Jafarpour and Samina Raza Abidi and William Van Woensel and Syed Sibte Raza Abidi},
url = {https://authors.elsevier.com/a/1Yf0D3KEGa1e9B},
doi = {10.1016/j.artmed.2019.02.003},
issn = {0933-3657},
year = {2019},
date = {2019-01-01},
journal = {Artificial Intelligence in Medicine},
volume = {94},
pages = {117-137},
abstract = {Patients with multiple medical conditions (comorbidity) pose major challenges to clinical decision support systems, since the different Clinical Practice Guidelines (CPG) often involve adverse interactions, such as drug-drug or drug-disease interactions. Moreover, opportunities often exist for optimizing care and resources across multiple CPG. These challenges have been taken up in the state of the art, with many approaches focusing on the static integration of comorbid CIG. Nevertheless, we observe that many aspects often change dynamically over time, in ways that cannot be foreseen – such as delays in care tasks, resource availability, test outcomes, and acute comorbid conditions. To ensure the clinical safety and effectiveness of integrating multiple comorbid CIG, these execution-time difficulties must be considered. Further, when dealing with comorbid conditions, we remark that clinical practitioners typically consider multiple complex solutions, depending on the patient’s health profile. Hence, execution-time flexibility, based on dynamic health parameters, is needed to effectively and safely cope with comorbid conditions. In this work, we introduce a flexible, knowledge-driven and execution-time approach to comorbid CIG integration, based on an OWL ontology with clearly defined integration semantics.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Patients with multiple medical conditions (comorbidity) pose major challenges to clinical decision support systems, since the different Clinical Practice Guidelines (CPG) often involve adverse interactions, such as drug-drug or drug-disease interactions. Moreover, opportunities often exist for optimizing care and resources across multiple CPG. These challenges have been taken up in the state of the art, with many approaches focusing on the static integration of comorbid CIG. Nevertheless, we observe that many aspects often change dynamically over time, in ways that cannot be foreseen – such as delays in care tasks, resource availability, test outcomes, and acute comorbid conditions. To ensure the clinical safety and effectiveness of integrating multiple comorbid CIG, these execution-time difficulties must be considered. Further, when dealing with comorbid conditions, we remark that clinical practitioners typically consider multiple complex solutions, depending on the patient’s health profile. Hence, execution-time flexibility, based on dynamic health parameters, is needed to effectively and safely cope with comorbid conditions. In this work, we introduce a flexible, knowledge-driven and execution-time approach to comorbid CIG integration, based on an OWL ontology with clearly defined integration semantics.
Patrice C Roy; William Van Woensel; Andy Wilcox; Syed Sibte Raza Abidi
Mobile Indoor Localization with Bluetooth Beacons in a Pediatric Emergency Department Using Clustering, Rule-based Classification and High-level Heuristics Proceedings Article
In: 17th Conf. on Artificial Intelligence in Medicine (AIME2019), Poznan, Poland, 2019.
BibTeX | Tags:
@inproceedings{Roy2019b,
title = {Mobile Indoor Localization with Bluetooth Beacons in a Pediatric Emergency Department Using Clustering, Rule-based Classification and High-level Heuristics},
author = {Patrice C Roy and William {Van Woensel} and Andy Wilcox and Syed Sibte Raza Abidi},
year = {2019},
date = {2019-01-01},
booktitle = {17th Conf. on Artificial Intelligence in Medicine (AIME2019)},
address = {Poznan, Poland},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Doerthe Arndt; William Van Woensel
Towards Supporting Multiple Semantics of Named Graphs Using N3 Rules Proceedings Article
In: Proceedings of the 13th RuleML+RR 2019 Doctoral Consortium and Rule Challenge, September 16-19, 2019 - Bolzano, Italy, September 16-24, 2019, CEUR-WS.org, 2019.
Abstract | Links | BibTeX | Tags:
@inproceedings{DBLP:conf/ruleml/ArndtW19,
title = {Towards Supporting Multiple Semantics of Named Graphs Using N3 Rules},
author = {Doerthe Arndt and William Van Woensel},
url = {http://ceur-ws.org/Vol-2438/paper6.pdf},
year = {2019},
date = {2019-01-01},
booktitle = {Proceedings of the 13th RuleML+RR 2019 Doctoral Consortium and Rule Challenge, September 16-19, 2019 - Bolzano, Italy, September 16-24, 2019},
volume = {2438},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
abstract = {Semantic Web applications often require the partitioning of triples into subgraphs, and then associating them with useful metadata(e.g., provenance). This led to the introduction of RDF datasets, with each RDF dataset comprising a default graph and zero or more named graphs. However, due to differences in RDF implementations, no consensus could be reached on a standard semantics; and a range of different dataset semantics are currently assumed. For an RDF system not be limited to only a subset of online RDF datasets, the system would need to be extended to support different dataset semantics—exactly the problem that eluded consensus before. In this paper, we transpose this problem to Notation3 Logic, an RDF-based rule language that similarly allows citing graphs within RDF documents. We propose a solution where an N3 author can directly indicate the intended semantics of a cited graph—possibly, combining multiple semantics within a single document. We supply an initial set of companion N3 rules, which implement a number of RDF dataset semantics, which allow an N3-compliant system to easily support multiple different semantics.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Semantic Web applications often require the partitioning of triples into subgraphs, and then associating them with useful metadata(e.g., provenance). This led to the introduction of RDF datasets, with each RDF dataset comprising a default graph and zero or more named graphs. However, due to differences in RDF implementations, no consensus could be reached on a standard semantics; and a range of different dataset semantics are currently assumed. For an RDF system not be limited to only a subset of online RDF datasets, the system would need to be extended to support different dataset semantics—exactly the problem that eluded consensus before. In this paper, we transpose this problem to Notation3 Logic, an RDF-based rule language that similarly allows citing graphs within RDF documents. We propose a solution where an N3 author can directly indicate the intended semantics of a cited graph—possibly, combining multiple semantics within a single document. We supply an initial set of companion N3 rules, which implement a number of RDF dataset semantics, which allow an N3-compliant system to easily support multiple different semantics.
2018
William Van Woensel; Syed Sibte Raza Abidi
Benchmarking Semantic Reasoning on Mobile Platforms: Towards Optimization Using OWL2 RL Journal Article
In: Semantic Web Journal, 2018.
Abstract | Links | BibTeX | Tags: Mobile Computing, OWL2 RL, Semantic Web reasoning
@article{SWJ-WVW-2018,
title = {Benchmarking Semantic Reasoning on Mobile Platforms: Towards Optimization Using OWL2 RL},
author = {William Van Woensel and Syed Sibte Raza Abidi},
url = {http://www.semantic-web-journal.net/system/files/swj1881.pdf},
year = {2018},
date = {2018-08-06},
journal = {Semantic Web Journal},
abstract = {Mobile hardware has advanced to a point where apps may consume the Semantic Web of Data, as exemplified in domains such as mobile context-awareness, m-Health, m-Tourism and augmented reality. However, recent work shows that the performance of ontology-based reasoning, an essential Semantic Web building block, still leaves much to be desired on mobile platforms. This presents a clear need to provide developers with the ability to benchmark mobile reasoning performance, based on their particular application scenarios, i.e., including reasoning tasks, process flows and datasets, to establish the feasibility of mobile deployment. In this regard, we present a mobile benchmark framework called MobiBench to help developers to benchmark semantic reasoners on mobile platforms. To realize efficient mobile, ontology-based reasoning, OWL2 RL is a promising solution since it (a) trades expressivity for scalability, which is important on resource-constrained platforms; and (b) provides unique opportunities for optimization due to its rule-based axiomatization. In this vein, we propose selections of OWL2 RL rule subsets for optimization purposes, based on several orthogonal dimensions. We extended MobiBench to support OWL2 RL and the proposed ruleset selections, and benchmarked multiple OWL2 RL-enabled rule engines and OWL reasoners on a mobile platform. Our results show significant performance improvements by applying OWL2 RL rule subsets, allowing performant reasoning for small datasets on mobile systems.},
keywords = {Mobile Computing, OWL2 RL, Semantic Web reasoning},
pubstate = {published},
tppubtype = {article}
}
Mobile hardware has advanced to a point where apps may consume the Semantic Web of Data, as exemplified in domains such as mobile context-awareness, m-Health, m-Tourism and augmented reality. However, recent work shows that the performance of ontology-based reasoning, an essential Semantic Web building block, still leaves much to be desired on mobile platforms. This presents a clear need to provide developers with the ability to benchmark mobile reasoning performance, based on their particular application scenarios, i.e., including reasoning tasks, process flows and datasets, to establish the feasibility of mobile deployment. In this regard, we present a mobile benchmark framework called MobiBench to help developers to benchmark semantic reasoners on mobile platforms. To realize efficient mobile, ontology-based reasoning, OWL2 RL is a promising solution since it (a) trades expressivity for scalability, which is important on resource-constrained platforms; and (b) provides unique opportunities for optimization due to its rule-based axiomatization. In this vein, we propose selections of OWL2 RL rule subsets for optimization purposes, based on several orthogonal dimensions. We extended MobiBench to support OWL2 RL and the proposed ruleset selections, and benchmarked multiple OWL2 RL-enabled rule engines and OWL reasoners on a mobile platform. Our results show significant performance improvements by applying OWL2 RL rule subsets, allowing performant reasoning for small datasets on mobile systems.
Hossein Mohammadhassanzadeh; Samina Abidi; William Van Woensel; Syed Sibte Raza Abidi
Investigating Plausible Reasoning over Knowledge Graphs for Semantics-based Health Data Analytics Proceedings Article
In: Data Exploration in the Web 3.0 Age (DEW) conference track at 27th IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE’18), IEEE, Paris, France, 2018.
Links | BibTeX | Tags: Plausible reasoning, Query Rewriting, Semantic Analytics, Semantic Web reasoning
@inproceedings{Mohammadhassanzadeh;2018,
title = {Investigating Plausible Reasoning over Knowledge Graphs for Semantics-based Health Data Analytics},
author = {Hossein Mohammadhassanzadeh and Samina Abidi and William Van Woensel and Syed Sibte Raza Abidi},
url = {https://ieeexplore.ieee.org/document/8495925},
year = {2018},
date = {2018-06-27},
booktitle = {Data Exploration in the Web 3.0 Age (DEW) conference track at 27th IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE’18)},
publisher = {IEEE},
address = {Paris, France},
keywords = {Plausible reasoning, Query Rewriting, Semantic Analytics, Semantic Web reasoning},
pubstate = {published},
tppubtype = {inproceedings}
}
William Van Woensel; Syed Sibte Raza Abidi
Optimizing Semantic Reasoning on Memory-Constrained Platforms using the RETE Algorithm Proceedings Article
In: 15th Extended Semantic Web Conference (ESWC 2018), pp. 682-696, Springer LNCS, Heraklion, Greece, 2018.
Abstract | Links | BibTeX | Tags: Mobile Computing, OWL2 RL, RETE, Semantic Web reasoning
@inproceedings{ESWC-WVW-2018,
title = {Optimizing Semantic Reasoning on Memory-Constrained Platforms using the RETE Algorithm},
author = {William Van Woensel and Syed Sibte Raza Abidi},
doi = {10.1007/978-3-319-93417-4_44},
year = {2018},
date = {2018-06-07},
booktitle = {15th Extended Semantic Web Conference (ESWC 2018)},
pages = {682-696},
publisher = {Springer LNCS},
address = {Heraklion, Greece},
abstract = {Mobile hardware improvements have opened the door for deploying rule systems on ubiquitous, mobile platforms. By executing rule-based tasks locally, less re-mote (cloud) resources are needed, bandwidth usage is reduced, and local, time-sensitive tasks are no longer influenced by network conditions. Further, with data being increasingly published in semantic format, an opportunity arises for rule systems to leverage the embedded semantics of semantic, ontology-based data. To support this kind of ontology-based reasoning in rule systems, rule-based axiomatizations of ontology semantics can be utilized (e.g., OWL 2 RL). Nonetheless, recent benchmarks have found that any kind of ontology-based reasoning on mobile platforms still lacks scalability, at least when directly re-using existing (PC- or server-based) technologies. To create a tailored solution for resource-constrained platforms, we propose changes to RETE, the mainstay algorithm for production rule systems. In particular, we present an adapted algorithm that, by selectively pooling RETE memories, aims to better balance memory usage with performance. Further, we show that this algorithm is well-suited towards many typical Semantic Web scenarios. Using our custom algorithm, we perform an extensive evaluation of semantic reasoning both on the PC and mobile platform.},
keywords = {Mobile Computing, OWL2 RL, RETE, Semantic Web reasoning},
pubstate = {published},
tppubtype = {inproceedings}
}
Mobile hardware improvements have opened the door for deploying rule systems on ubiquitous, mobile platforms. By executing rule-based tasks locally, less re-mote (cloud) resources are needed, bandwidth usage is reduced, and local, time-sensitive tasks are no longer influenced by network conditions. Further, with data being increasingly published in semantic format, an opportunity arises for rule systems to leverage the embedded semantics of semantic, ontology-based data. To support this kind of ontology-based reasoning in rule systems, rule-based axiomatizations of ontology semantics can be utilized (e.g., OWL 2 RL). Nonetheless, recent benchmarks have found that any kind of ontology-based reasoning on mobile platforms still lacks scalability, at least when directly re-using existing (PC- or server-based) technologies. To create a tailored solution for resource-constrained platforms, we propose changes to RETE, the mainstay algorithm for production rule systems. In particular, we present an adapted algorithm that, by selectively pooling RETE memories, aims to better balance memory usage with performance. Further, we show that this algorithm is well-suited towards many typical Semantic Web scenarios. Using our custom algorithm, we perform an extensive evaluation of semantic reasoning both on the PC and mobile platform.
2017
William Van Woensel; Wasif Baig; Syed Sibte Raza Abidi; Samina Abidi
A Semantic Web Framework for Behavioral User Modeling and Action Planning for Personalized Behavior Modification Proceedings Article
In: 10th International Conference on Semantic Web Applications and Tools for Life Sciences, CEUR, Rome, Italy, 2017.
Links | BibTeX | Tags: Behaviour Modelling, Behavioural Change Theory, Personalized Medicine
@inproceedings{SCT2017,
title = {A Semantic Web Framework for Behavioral User Modeling and Action Planning for Personalized Behavior Modification},
author = {William Van Woensel and Wasif Baig and Syed Sibte Raza Abidi and Samina Abidi},
url = {https://niche.cs.dal.ca/wp-content/uploads/2017/12/paper-21-camera-ready-1.pdf},
year = {2017},
date = {2017-12-06},
booktitle = {10th International Conference on Semantic Web Applications and Tools for Life Sciences},
publisher = {CEUR},
address = {Rome, Italy},
keywords = {Behaviour Modelling, Behavioural Change Theory, Personalized Medicine},
pubstate = {published},
tppubtype = {inproceedings}
}
William Van Woensel; Patrice C. Roy; Syed Sibte Raza Abidi
Achieving Pro-Active Guidance of Patients through ADL via Knowledge-Driven Activity Recognition and Complex Semantic Workflows Proceedings Article
In: 10th International Conference on Semantic Web Applications and Tools for Life Sciences, CEUR, Rome, Italy, 2017.
Links | BibTeX | Tags: Activity Recognition, Ambient Intelligence, Semantic Web reasoning
@inproceedings{ADL201,
title = {Achieving Pro-Active Guidance of Patients through ADL via Knowledge-Driven Activity Recognition and Complex Semantic Workflows},
author = {William Van Woensel and Patrice C. Roy and Syed Sibte Raza Abidi},
url = {https://niche.cs.dal.ca/wp-content/uploads/2017/12/paper_camera-ready.pdf},
year = {2017},
date = {2017-12-06},
booktitle = {10th International Conference on Semantic Web Applications and Tools for Life Sciences},
publisher = {CEUR},
address = {Rome, Italy},
keywords = {Activity Recognition, Ambient Intelligence, Semantic Web reasoning},
pubstate = {published},
tppubtype = {inproceedings}
}
Hossein Mohammadhassanzadeh; William Van Woensel; Samina Raza Abidi; Syed Sibte Raza Abidi
Semantics-based Plausible Reasoning to Extend the Knowledge Coverage of Medical Knowledge Bases for Improved Clinical Decision Support Journal Article
In: Journal of BioData Mining, vol. 10, no. 7, 2017.
Abstract | Links | BibTeX | Tags: Analogical reasoning, Inductive generalization, Medical knowledge bases, Plausible reasoning, Semantic Web reasoning
@article{Mohammadhassanzadeh2017,
title = {Semantics-based Plausible Reasoning to Extend the Knowledge Coverage of Medical Knowledge Bases for Improved Clinical Decision Support},
author = {Hossein Mohammadhassanzadeh and William Van Woensel and Samina Raza Abidi and Syed Sibte Raza Abidi},
url = {http://rdcu.be/paPY},
doi = {10.1186/s13040-017-0123-y},
year = {2017},
date = {2017-02-10},
journal = {Journal of BioData Mining},
volume = {10},
number = {7},
abstract = {Background
Capturing complete medical knowledge is challenging-often due to incomplete patient Electronic Health Records (EHR), but also because of valuable, tacit medical knowledge hidden away in physicians’ experiences. To extend the coverage of incomplete medical knowledge-based systems beyond their deductive closure, and thus enhance their decision-support capabilities, we argue that innovative, multi-strategy reasoning approaches should be applied. In particular, plausible reasoning mechanisms apply patterns from human thought processes, such as generalization, similarity and interpolation, based on attributional, hierarchical, and relational knowledge. Plausible reasoning mechanisms include inductive reasoning, which generalizes the commonalities among the data to induce new rules, and analogical reasoning, which is guided by data similarities to infer new facts. By further leveraging rich, biomedical Semantic Web ontologies to represent medical knowledge, both known and tentative, we increase the accuracy and expressivity of plausible reasoning, and cope with issues such as data heterogeneity, inconsistency and interoperability. In this paper, we present a Semantic Web-based, multi-strategy reasoning approach, which integrates deductive and plausible reasoning and exploits Semantic Web technology to solve complex clinical decision support queries.
Results
We evaluated our system using a real-world medical dataset of patients with hepatitis, from which we randomly removed different percentages of data (5%, 10%, 15%, and 20%) to reflect scenarios with increasing amounts of incomplete medical knowledge. To increase the reliability of the results, we generated 5 independent datasets for each percentage of missing values, which resulted in 20 experimental datasets (in addition to the original dataset). The results show that plausibly inferred knowledge extends the coverage of the knowledge base by, on average, 2%, 7%, 12%, and 16% for datasets with, respectively, 5%, 10%, 15%, and 20% of missing values. This expansion in the KB coverage allowed solving complex disease diagnostic queries that were previously unresolvable, without losing the correctness of the answers. However, compared to deductive reasoning, data-intensive plausible reasoning mechanisms yield a significant performance overhead.
Conclusions
We observed that plausible reasoning approaches, by generating tentative inferences and leveraging domain knowledge of experts, allow us to extend the coverage of medical knowledge bases, resulting in improved clinical decision support. Second, by leveraging OWL ontological knowledge, we are able to increase the expressivity and accuracy of plausible reasoning methods. Third, our approach is applicable to clinical decision support systems for a range of chronic diseases.},
keywords = {Analogical reasoning, Inductive generalization, Medical knowledge bases, Plausible reasoning, Semantic Web reasoning},
pubstate = {published},
tppubtype = {article}
}
Background
Capturing complete medical knowledge is challenging-often due to incomplete patient Electronic Health Records (EHR), but also because of valuable, tacit medical knowledge hidden away in physicians’ experiences. To extend the coverage of incomplete medical knowledge-based systems beyond their deductive closure, and thus enhance their decision-support capabilities, we argue that innovative, multi-strategy reasoning approaches should be applied. In particular, plausible reasoning mechanisms apply patterns from human thought processes, such as generalization, similarity and interpolation, based on attributional, hierarchical, and relational knowledge. Plausible reasoning mechanisms include inductive reasoning, which generalizes the commonalities among the data to induce new rules, and analogical reasoning, which is guided by data similarities to infer new facts. By further leveraging rich, biomedical Semantic Web ontologies to represent medical knowledge, both known and tentative, we increase the accuracy and expressivity of plausible reasoning, and cope with issues such as data heterogeneity, inconsistency and interoperability. In this paper, we present a Semantic Web-based, multi-strategy reasoning approach, which integrates deductive and plausible reasoning and exploits Semantic Web technology to solve complex clinical decision support queries.
Results
We evaluated our system using a real-world medical dataset of patients with hepatitis, from which we randomly removed different percentages of data (5%, 10%, 15%, and 20%) to reflect scenarios with increasing amounts of incomplete medical knowledge. To increase the reliability of the results, we generated 5 independent datasets for each percentage of missing values, which resulted in 20 experimental datasets (in addition to the original dataset). The results show that plausibly inferred knowledge extends the coverage of the knowledge base by, on average, 2%, 7%, 12%, and 16% for datasets with, respectively, 5%, 10%, 15%, and 20% of missing values. This expansion in the KB coverage allowed solving complex disease diagnostic queries that were previously unresolvable, without losing the correctness of the answers. However, compared to deductive reasoning, data-intensive plausible reasoning mechanisms yield a significant performance overhead.
Conclusions
We observed that plausible reasoning approaches, by generating tentative inferences and leveraging domain knowledge of experts, allow us to extend the coverage of medical knowledge bases, resulting in improved clinical decision support. Second, by leveraging OWL ontological knowledge, we are able to increase the expressivity and accuracy of plausible reasoning methods. Third, our approach is applicable to clinical decision support systems for a range of chronic diseases.
Capturing complete medical knowledge is challenging-often due to incomplete patient Electronic Health Records (EHR), but also because of valuable, tacit medical knowledge hidden away in physicians’ experiences. To extend the coverage of incomplete medical knowledge-based systems beyond their deductive closure, and thus enhance their decision-support capabilities, we argue that innovative, multi-strategy reasoning approaches should be applied. In particular, plausible reasoning mechanisms apply patterns from human thought processes, such as generalization, similarity and interpolation, based on attributional, hierarchical, and relational knowledge. Plausible reasoning mechanisms include inductive reasoning, which generalizes the commonalities among the data to induce new rules, and analogical reasoning, which is guided by data similarities to infer new facts. By further leveraging rich, biomedical Semantic Web ontologies to represent medical knowledge, both known and tentative, we increase the accuracy and expressivity of plausible reasoning, and cope with issues such as data heterogeneity, inconsistency and interoperability. In this paper, we present a Semantic Web-based, multi-strategy reasoning approach, which integrates deductive and plausible reasoning and exploits Semantic Web technology to solve complex clinical decision support queries.
Results
We evaluated our system using a real-world medical dataset of patients with hepatitis, from which we randomly removed different percentages of data (5%, 10%, 15%, and 20%) to reflect scenarios with increasing amounts of incomplete medical knowledge. To increase the reliability of the results, we generated 5 independent datasets for each percentage of missing values, which resulted in 20 experimental datasets (in addition to the original dataset). The results show that plausibly inferred knowledge extends the coverage of the knowledge base by, on average, 2%, 7%, 12%, and 16% for datasets with, respectively, 5%, 10%, 15%, and 20% of missing values. This expansion in the KB coverage allowed solving complex disease diagnostic queries that were previously unresolvable, without losing the correctness of the answers. However, compared to deductive reasoning, data-intensive plausible reasoning mechanisms yield a significant performance overhead.
Conclusions
We observed that plausible reasoning approaches, by generating tentative inferences and leveraging domain knowledge of experts, allow us to extend the coverage of medical knowledge bases, resulting in improved clinical decision support. Second, by leveraging OWL ontological knowledge, we are able to increase the expressivity and accuracy of plausible reasoning methods. Third, our approach is applicable to clinical decision support systems for a range of chronic diseases.
Stefania Costantini; Enrico Franconi; William Van Woensel; Roman Kontchakov; Fariba Sadri; Dumitru Roman (Ed.)
Springer, vol. 10364, 2017, ISBN: 978-3-319-61251-5.
@proceedings{DBLP:conf/ruleml/2017,
title = {Rules and Reasoning - International Joint Conference, RuleML+RR 2017, London, UK, July 12-15, 2017, Proceedings},
editor = {Stefania Costantini and Enrico Franconi and William Van Woensel and Roman Kontchakov and Fariba Sadri and Dumitru Roman},
url = {https://doi.org/10.1007/978-3-319-61252-2},
doi = {10.1007/978-3-319-61252-2},
isbn = {978-3-319-61251-5},
year = {2017},
date = {2017-01-01},
volume = {10364},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
Nick Bassiliades; Antonis Bikakis; Stefania Costantini; Enrico Franconi; Adrian Giurca; Roman Kontchakov; Theodore Patkos; Fariba Sadri; William Van Woensel (Ed.)
CEUR-WS.org, vol. 1875, 2017.
@proceedings{DBLP:conf/ruleml/2017s,
title = {Proceedings of the Doctoral Consortium, Challenge, Industry Track, Tutorials and Posters @ RuleML+RR 2017 hosted by International Joint Conference on Rules and Reasoning 2017 (RuleML+RR 2017), London, UK, July 11-15, 2017},
editor = {Nick Bassiliades and Antonis Bikakis and Stefania Costantini and Enrico Franconi and Adrian Giurca and Roman Kontchakov and Theodore Patkos and Fariba Sadri and William Van Woensel},
url = {http://ceur-ws.org/Vol-1875},
year = {2017},
date = {2017-01-01},
volume = {1875},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
2016
Hossein Mohammadhassanzadeh; William Van Woensel; Samina Raza Abidi; Syed Sibte Raza Abidi
A Semantic Web-based Approach to Plausible Reasoning for Improving Clinical Knowledge Engineering Proceedings Article
In: IEEE International Conference on Biomedical and Health Informatics, Las Vegas, 2016.
Abstract | BibTeX | Tags: Clinical Decision Support Systems, Plausbile Reasoning, Semantic Web
@inproceedings{Mohammadhassanzadeh2016,
title = {A Semantic Web-based Approach to Plausible Reasoning for Improving Clinical Knowledge Engineering},
author = {Hossein Mohammadhassanzadeh and William Van Woensel and Samina Raza Abidi and Syed Sibte Raza Abidi},
year = {2016},
date = {2016-02-24},
urldate = {2016-02-24},
booktitle = {IEEE International Conference on Biomedical and Health Informatics, Las Vegas},
abstract = {In this paper, we present a semantic web based knowledge engineering approach to extend the coverage of medical knowledge-based systems in order to solve complex medical queries that demand the integration of deterministic and plausible knowledge. We leverage plausible reasoning mechanisms, which exploit associations between the underlying domain-specific data, as well as tentative domain knowledge, to extend the coverage of a medical knowledge base. We demonstrate that Semantic Web technologies, due to their efficient solutions for federated data management and built-in DL-based inferencing methods, offer useful opportunities to support plausible reasoning for medical decision support tasks. We evaluated our multi-strategy medical reasoning approach using real-world medical data. Our results illustrate that plausible reasoning improved the knowledge coverage of the original medical knowledge base by 10-12%, and in turn helped to solve complex disease diagnostic queries.},
keywords = {Clinical Decision Support Systems, Plausbile Reasoning, Semantic Web},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we present a semantic web based knowledge engineering approach to extend the coverage of medical knowledge-based systems in order to solve complex medical queries that demand the integration of deterministic and plausible knowledge. We leverage plausible reasoning mechanisms, which exploit associations between the underlying domain-specific data, as well as tentative domain knowledge, to extend the coverage of a medical knowledge base. We demonstrate that Semantic Web technologies, due to their efficient solutions for federated data management and built-in DL-based inferencing methods, offer useful opportunities to support plausible reasoning for medical decision support tasks. We evaluated our multi-strategy medical reasoning approach using real-world medical data. Our results illustrate that plausible reasoning improved the knowledge coverage of the original medical knowledge base by 10-12%, and in turn helped to solve complex disease diagnostic queries.
William Van Woensel; Sven Casteleyn
A Mobile Query Service for Integrated Access to Large Numbers of Online Semantic Web Data Sources Journal Article
In: Web Semant., vol. 36, no. C, pp. 58–76, 2016, ISSN: 1570-8268.
Links | BibTeX | Tags: Cache replacement, Data caching, Data indexing, Data integration, Mobile Computing, Open world assumption
@article{VanWoensel:2016:MQS:2895617.2895676,
title = {A Mobile Query Service for Integrated Access to Large Numbers of Online Semantic Web Data Sources},
author = { William Van Woensel and Sven Casteleyn},
url = {https://niche.cs.dal.ca/wp-content/uploads/2016/05/A-Mobile-Query-Service-for-Integrated-Access-to-Large-Numbers-of-Online-Semantic-Web-Data-Sources-1.pdf},
doi = {10.1016/j.websem.2015.10.002},
issn = {1570-8268},
year = {2016},
date = {2016-01-01},
journal = {Web Semant.},
volume = {36},
number = {C},
pages = {58--76},
publisher = {Elsevier Science Publishers B. V.},
address = {Amsterdam, The Netherlands, The Netherlands},
keywords = {Cache replacement, Data caching, Data indexing, Data integration, Mobile Computing, Open world assumption},
pubstate = {published},
tppubtype = {article}
}
William Van Woensel; Patrice C. Roy; Syed Sibte Raza Abidi
SmartRL: A Context-Sensitive, Ontology-Based Rule Language for Assisted Living in Smart Environments Proceedings Article
In: Rule Technologies. Research, Tools, and Applications - 10th International Symposium, RuleML 2016, Stony Brook, NY, USA, July 6-9, 2016. Proceedings, pp. 341–349, 2016.
@inproceedings{DBLP:conf/ruleml/WoenselRA16,
title = {SmartRL: A Context-Sensitive, Ontology-Based Rule Language for Assisted Living in Smart Environments},
author = {William Van Woensel and Patrice C. Roy and Syed Sibte Raza Abidi},
url = {http://dx.doi.org/10.1007/978-3-319-42019-6_22},
doi = {10.1007/978-3-319-42019-6_22},
year = {2016},
date = {2016-01-01},
booktitle = {Rule Technologies. Research, Tools, and Applications - 10th International Symposium, RuleML 2016, Stony Brook, NY, USA, July 6-9, 2016. Proceedings},
pages = {341--349},
crossref = {DBLP:conf/ruleml/2016},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2015
William Van Woensel
Mobile Semantic Query Distribution with Graph-Based Outsourcing of Subqueries Proceedings Article
In: Proceedings of International Workshop on Mobile Deployment of Semantic Technologies (MoDeST 2015), 2015.
Links | BibTeX | Tags: Mobile Computing, Querying Distributed RDF
@inproceedings{van_woensel_modest2015,
title = {Mobile Semantic Query Distribution with Graph-Based Outsourcing of Subqueries},
author = {William Van Woensel},
url = {http://web.cs.dal.ca/~woensel/paper/Mobile Semantic Query Distribution with Graph-Based Outsourcing of Subqueries.pdf},
year = {2015},
date = {2015-10-11},
booktitle = {Proceedings of International Workshop on Mobile Deployment of Semantic Technologies (MoDeST 2015)},
keywords = {Mobile Computing, Querying Distributed RDF},
pubstate = {published},
tppubtype = {inproceedings}
}
William Van Woensel; Hossein Mohammadhassanzadeh; Samina Raza Abidi; Syed Sibte Raza Abidi
Multi-Strategy Semantic Web Reasoning for Medical Knowledge Bases Proceedings Article
In: Proceedings of International Workshop on Biomedical Data Mining, Modeling, and Semantic Integration: A Promising Approach to Solving Unmet Medical Needs (BDM2I2015), 2015.
Links | BibTeX | Tags: Health Informatics, Knowledge Based Systems, Ontology, Semantic Web
@inproceedings{DBLP:conf/semweb/WoenselMAA15,
title = {Multi-Strategy Semantic Web Reasoning for Medical Knowledge Bases},
author = {William Van Woensel and Hossein Mohammadhassanzadeh and Samina Raza Abidi and Syed Sibte Raza Abidi},
url = {http://ceur-ws.org/Vol-1428/BDM2I_2015_paper_8.pdf},
year = {2015},
date = {2015-10-11},
booktitle = {Proceedings of International Workshop on Biomedical Data Mining, Modeling, and Semantic Integration: A Promising Approach to Solving Unmet Medical Needs (BDM2I2015)},
keywords = {Health Informatics, Knowledge Based Systems, Ontology, Semantic Web},
pubstate = {published},
tppubtype = {inproceedings}
}
Amina Russell; William Van Woensel; Samina Raza Abidi
The Shared Decision Making Frontier: Managing Non-Critical Chronic Illness by Combining Behavioural & Decision Theory with Online Technology Proceedings Article
In: MEDINFO 2015: eHealth-enabled Health, 2015.
Abstract | Links | BibTeX | Tags: Biomedical Technology Intervention, Choice Architecture, Chronic Illness, Shared Decision Making
@inproceedings{arussell-medinfo15,
title = {The Shared Decision Making Frontier: Managing Non-Critical Chronic Illness by Combining Behavioural & Decision Theory with Online Technology},
author = {Amina Russell and William Van Woensel and Samina Raza Abidi},
url = {http://web.cs.dal.ca/~woensel/paper/The Shared Decision Making Frontier a Feasibility and Usability Study for Managing Non-Critical Chronic Illness by Combining Behavioural & Decision Theory with Online Technology.pdf},
year = {2015},
date = {2015-09-19},
booktitle = {MEDINFO 2015: eHealth-enabled Health},
abstract = {Objective: To determine if shared decisions for managing non-critical chronic illness, made through an online biomedical technology intervention, proves feasible and usable. The technology intervention incorporates behavioural and decision theory to increase patient engagement, and ultimately long term adherence to health behaviour change.
Method: We devised the iheart web intervention as a “proof of concept” in five phases: 1) conceptual, 2) design, 3) application development, 4) testing and 5) study assessment. The implementation incorporates the Vaadin web application framework, Drools, EclipseLink and a MySQL database.
Results and discussion: Two-thirds of the study participants favoured the technology intervention, based on Likert-scale questions from a post-study questionnaire. Qualitative analysis of think aloud feedback, video screen captures and open-ended questions from the post-study questionnaire, uncovered six main areas or themes for improvement.
Conclusion: Online shared decisions for managing a non-critical chronic illness proved feasible and usable through the iheart web intervention. Areas needing improvement have been identified for the next application revision. An efficacy study is recommended as a next step.
},
keywords = {Biomedical Technology Intervention, Choice Architecture, Chronic Illness, Shared Decision Making},
pubstate = {published},
tppubtype = {inproceedings}
}
Objective: To determine if shared decisions for managing non-critical chronic illness, made through an online biomedical technology intervention, proves feasible and usable. The technology intervention incorporates behavioural and decision theory to increase patient engagement, and ultimately long term adherence to health behaviour change.
Method: We devised the iheart web intervention as a “proof of concept” in five phases: 1) conceptual, 2) design, 3) application development, 4) testing and 5) study assessment. The implementation incorporates the Vaadin web application framework, Drools, EclipseLink and a MySQL database.
Results and discussion: Two-thirds of the study participants favoured the technology intervention, based on Likert-scale questions from a post-study questionnaire. Qualitative analysis of think aloud feedback, video screen captures and open-ended questions from the post-study questionnaire, uncovered six main areas or themes for improvement.
Conclusion: Online shared decisions for managing a non-critical chronic illness proved feasible and usable through the iheart web intervention. Areas needing improvement have been identified for the next application revision. An efficacy study is recommended as a next step.
Method: We devised the iheart web intervention as a “proof of concept” in five phases: 1) conceptual, 2) design, 3) application development, 4) testing and 5) study assessment. The implementation incorporates the Vaadin web application framework, Drools, EclipseLink and a MySQL database.
Results and discussion: Two-thirds of the study participants favoured the technology intervention, based on Likert-scale questions from a post-study questionnaire. Qualitative analysis of think aloud feedback, video screen captures and open-ended questions from the post-study questionnaire, uncovered six main areas or themes for improvement.
Conclusion: Online shared decisions for managing a non-critical chronic illness proved feasible and usable through the iheart web intervention. Areas needing improvement have been identified for the next application revision. An efficacy study is recommended as a next step.
William Van Woensel; Patrice C. Roy; Samina Raza Abidi; Syed Sibte Raza Abidi
A Mobile and Intelligent Patient Diary for Chronic Disease Self-Management Proceedings Article
In: MEDINFO 2015: eHealth-enabled Health - Proceedings of the 15th World Congress on Health and Biomedical Informatics, Sao Paulo, Brazil, pp. 118–122, IOS Press, 2015, ISBN: 978-1-61499-563-0.
Links | BibTeX | Tags: Chronic Disease Management, Mobile Health, Patient Diary, Patient Empowerment, Self-Management
@inproceedings{DBLP:conf/medinfo/WoenselRAA15,
title = {A Mobile and Intelligent Patient Diary for Chronic Disease Self-Management},
author = {William Van Woensel and Patrice C. Roy and Samina Raza Abidi and Syed Sibte Raza Abidi},
url = {http://dx.doi.org/10.3233/978-1-61499-564-7-118},
doi = {10.3233/978-1-61499-564-7-118},
isbn = {978-1-61499-563-0},
year = {2015},
date = {2015-08-23},
booktitle = {MEDINFO 2015: eHealth-enabled Health - Proceedings of the 15th World Congress on Health and Biomedical Informatics, Sao Paulo, Brazil},
pages = {118--122},
publisher = {IOS Press},
keywords = {Chronic Disease Management, Mobile Health, Patient Diary, Patient Empowerment, Self-Management},
pubstate = {published},
tppubtype = {inproceedings}
}
2014
William Van Woensel
Mobile, Client-Side Discovery of Semantic Services in the Physical World Proceedings Article
In: 5th Atlantic Workshop on Semantics and Services, 2014.
@inproceedings{AWoSS2014,
title = {Mobile, Client-Side Discovery of Semantic Services in the Physical World},
author = { William Van Woensel},
url = {http://web.cs.dal.ca/~woensel/paper/Mobile Client-Side Discovery of Semantic Services in the Physical World.pdf},
year = {2014},
date = {2014-01-01},
booktitle = {5th Atlantic Workshop on Semantics and Services},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
William Van Woensel; Newres Al Haider; Ahmad Marwan Ahmad; Syed Sibte Raza Abidi
A Cross-Platform Benchmark Framework for Mobile Semantic Web Reasoning Engines Proceedings Article
In: The Semantic Web - ISWC 2014 - 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I, pp. 389–408, 2014.
@inproceedings{DBLP:conf/semweb/WoenselHAA14,
title = {A Cross-Platform Benchmark Framework for Mobile Semantic Web Reasoning Engines},
author = { William Van Woensel and Newres Al Haider and Ahmad Marwan Ahmad and Syed Sibte Raza Abidi},
url = {http://web.cs.dal.ca/~woensel/paper/A Cross-Platform Benchmark Framework for Mobile Semantic Web Reasoning Engines.pdf},
year = {2014},
date = {2014-01-01},
booktitle = {The Semantic Web - ISWC 2014 - 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I},
pages = {389--408},
crossref = {DBLP:conf/semweb/2014-1},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
William Van Woensel; Newres Al Haider; Patrice C. Roy; Ahmad Marwan Ahmad; Syed Sibte Raza Abidi
A Comparison of Mobile Rule Engines for Reasoning on Semantic Web Based Health Data Proceedings Article
In: Śl?zak, Dominik; Nguyen, Hung Son; Reformat, Marek; Jr., Eugene Santos (Ed.): 2014 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2014), pp. 126–133, IEEE Computer Society Press, Warsaw, Poland, 2014.
@inproceedings{WIC2014,
title = {A Comparison of Mobile Rule Engines for Reasoning on Semantic Web Based Health Data},
author = { William Van Woensel and Newres Al Haider and Patrice C. Roy and Ahmad Marwan Ahmad and Syed Sibte Raza Abidi},
editor = { Dominik Śl?zak and Hung Son Nguyen and Marek Reformat and Eugene Santos Jr.},
url = {http://web.cs.dal.ca/~woensel/paper/A Comparison of Mobile Rule Engines for Reasoning on Semantic Web Based Health Data.pdf},
year = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2014)},
pages = {126--133},
publisher = {IEEE Computer Society Press},
address = {Warsaw, Poland},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Patrice C. Roy; Newres Al Haider; William Van Woensel; Ahmad Marwan Ahmad; Syed Sibte Raza Abidi
Towards Guideline Compliant Clinical Decision Support System Integration in Smart and Mobile Environments: Formalizing and Using Clinical Guidelines For Diagnosing Sleep Apnea Proceedings Article
In: AAAI Workshop on Artificial Intelligence Applied to Assistive Technologies and Smart Environments (ATSE 2014), AAAI Press, Quebec, Canada, 2014.
Links | BibTeX | Tags: Clinical Decision Support Systems, Clinical Practice Guidelines, Health Informatics
@inproceedings{Roy2014,
title = {Towards Guideline Compliant Clinical Decision Support System Integration in Smart and Mobile Environments: Formalizing and Using Clinical Guidelines For Diagnosing Sleep Apnea},
author = { Patrice C. Roy and Newres Al Haider and William Van Woensel and Ahmad Marwan Ahmad and Syed Sibte Raza Abidi},
url = {http://web.cs.dal.ca/~woensel/paper/Towards Guideline Compliant Clinical Decision Support System Integration in Smart and Mobile Environments.pdf},
year = {2014},
date = {2014-01-01},
booktitle = {AAAI Workshop on Artificial Intelligence Applied to Assistive Technologies and Smart Environments (ATSE 2014)},
publisher = {AAAI Press},
address = {Quebec, Canada},
keywords = {Clinical Decision Support Systems, Clinical Practice Guidelines, Health Informatics},
pubstate = {published},
tppubtype = {inproceedings}
}
2013
William Van Woensel
Mobile, client-side context-provisioning via the integrated querying of online semantic web data PhD Thesis
Vrije Universiteit Brussel, 2013.
Abstract | Links | BibTeX | Tags:
@phdthesis{VanWoensel2013,
title = {Mobile, client-side context-provisioning via the integrated querying of online semantic web data},
author = { William Van Woensel},
url = {http://web.cs.dal.ca/~woensel/paper/Mobile client-side context-provisioning via the integrated querying of online semantic web data.pdf},
year = {2013},
date = {2013-01-01},
school = {Vrije Universiteit Brussel},
abstract = {Mobile devices capabilities have increased tremendously over the last few years, enabling mobile users to run resource-heavy applications, such as route planners, web browsers and games, at any time and any place. Much work has been done to facilitate interaction with mobile applications, in mobile settings and on devices with relatively small screens and cumbersome input features. For instance, multi-touch, gesture and voice recognition are deployed to easily execute certain actions, while obtrusiveness of mobile interactions is adapted to suit the user's current situation (e.g., in a meeting). However, mobile interactions still have inherent limitations, mainly due to the fact they are mobile in the first place; mobile users often do not have the time, or the comfortable desktop-setting, to interact with mobile applications. In order to cope with such limitations, the mobile user's context can further be leveraged. Context is defined as any piece of information related to application interaction, including information on the user's surroundings as well as the user and device. By automatically presenting information and services suiting the user's current context, mobile interactions can be enhanced. For example, considering the mobile user's current surroundings and preferences, he can be notified of nearby shops selling items on his shopping list, or nearby public transportation stations leading back to his hotel. In this dissertation, we present a client-side framework to provision context in mobile settings, which exploits recent evolutions in mobile device technology and the World Wide Web. By leveraging increased mobile processing power and memory capacity, computationally intensive tasks, such as context interpretation, integration and dissemination, are performed locally on the mobile device itself. Furthermore, the machine-readable Semantic Web, the next step in the evolution of the Web, is utilized as an online platform for retrieving context data. Much useful context information, describing people, places and things in the user's vicinity, is already captured in small, machine-readable online web sources; including websites (e.g., shops, monuments) and online RDF files (e.g., person profiles). As websites are being increasingly semantically annotated, making the meaning of their content explicit, many of them have become fully-fledged semantic data sources as well. In order to achieve transparent, integrated query access to such small online semantic sources, this dissertation further presents a mobile, client-side query service. This query service comprises indexing and caching components to enable querying such an online dataset on mobile devices.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Mobile devices capabilities have increased tremendously over the last few years, enabling mobile users to run resource-heavy applications, such as route planners, web browsers and games, at any time and any place. Much work has been done to facilitate interaction with mobile applications, in mobile settings and on devices with relatively small screens and cumbersome input features. For instance, multi-touch, gesture and voice recognition are deployed to easily execute certain actions, while obtrusiveness of mobile interactions is adapted to suit the user's current situation (e.g., in a meeting). However, mobile interactions still have inherent limitations, mainly due to the fact they are mobile in the first place; mobile users often do not have the time, or the comfortable desktop-setting, to interact with mobile applications. In order to cope with such limitations, the mobile user's context can further be leveraged. Context is defined as any piece of information related to application interaction, including information on the user's surroundings as well as the user and device. By automatically presenting information and services suiting the user's current context, mobile interactions can be enhanced. For example, considering the mobile user's current surroundings and preferences, he can be notified of nearby shops selling items on his shopping list, or nearby public transportation stations leading back to his hotel. In this dissertation, we present a client-side framework to provision context in mobile settings, which exploits recent evolutions in mobile device technology and the World Wide Web. By leveraging increased mobile processing power and memory capacity, computationally intensive tasks, such as context interpretation, integration and dissemination, are performed locally on the mobile device itself. Furthermore, the machine-readable Semantic Web, the next step in the evolution of the Web, is utilized as an online platform for retrieving context data. Much useful context information, describing people, places and things in the user's vicinity, is already captured in small, machine-readable online web sources; including websites (e.g., shops, monuments) and online RDF files (e.g., person profiles). As websites are being increasingly semantically annotated, making the meaning of their content explicit, many of them have become fully-fledged semantic data sources as well. In order to achieve transparent, integrated query access to such small online semantic sources, this dissertation further presents a mobile, client-side query service. This query service comprises indexing and caching components to enable querying such an online dataset on mobile devices.