About Me
Research Interests
Machine Learning, Natural Language Processing, Data Visualization and Analytics, Big Data.
Publications
2021
D O'Sullivan; W Van Woensel; S Wilk; S W Tu; W Michalowski; S Abidi; M Carrier; R Edry; I Hochberg; S Kingwell; K Kogan; M Michalowski; H O'Sullivan; M Peleg
Towards a framework for comparing functionalities of multimorbidity clinical decision support: A literature-based feature set and benchmark cases Proceedings Article
In: AMIA 2021 Annual Symposium, San Diego, CA, 2021.
BibTeX | Tags: Clinical Decision Support Systems, Comorbidity
@inproceedings{OSullivan2021,
title = {Towards a framework for comparing functionalities of multimorbidity clinical decision support: A literature-based feature set and benchmark cases},
author = {D O'Sullivan and W Van Woensel and S Wilk and S W Tu and W Michalowski and S Abidi and M Carrier and R Edry and I Hochberg and S Kingwell and K Kogan and M Michalowski and H O'Sullivan and M Peleg},
year = {2021},
date = {2021-10-30},
urldate = {2021-01-01},
booktitle = {AMIA 2021 Annual Symposium},
address = {San Diego, CA},
keywords = {Clinical Decision Support Systems, Comorbidity},
pubstate = {published},
tppubtype = {inproceedings}
}
William Van Woensel; 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.
0000
William Van Woensel; Samina Raza Abidi; Syed Sibte Raza Abidi
Decision Support for Comorbid Conditions via Execution-Time Integration of Clinical Guidelines Using Transaction-based Semantics and Temporal Planning (under review) Journal Article
In: Artificial Intelligence in Medicine, 0000.
BibTeX | Tags: Clinical Decision Support Systems, Clinical Practice Guidelines, Comorbidity
@article{wvw_aiim_21,
title = {Decision Support for Comorbid Conditions via Execution-Time Integration of Clinical Guidelines Using Transaction-based Semantics and Temporal Planning (under review)},
author = {William Van Woensel and Samina Raza Abidi and Syed Sibte Raza Abidi},
journal = {Artificial Intelligence in Medicine},
keywords = {Clinical Decision Support Systems, Clinical Practice Guidelines, Comorbidity},
pubstate = {published},
tppubtype = {article}
}