Publications

@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}

}

@article{Abidi2018,

title = {A data mining framework for glaucoma decision support based on optic nerve image analysis using machine learning methods},

author = {Syed S.R. Abidi and Patrice C. Roy and Muhammad S. Shah and Jin Yu and Sanjun Yan},

doi = {10.1007/s41666-018-0028-7},

year  = {2018},

date = {2018-12-01},

journal = {Journal of Healthcare Informatics Research},

volume = {2},

number = {4},

pages = {370-401},

keywords = {Classification, Clustering, Data Mining, Decision Support Systems, Optic Nerve},

pubstate = {published},

tppubtype = {article}

}