Samina Abidi

@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}

}

@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}

}

@article{Jafarpour:IeeeJournalOfBiomedicalAndHealthInformat,

title = {Exploiting Semantic Web Technologies to Develop OWL-Based Clinical Practice Guideline Execution Engines},

author = {Borna Jafarpour and Syed Sibte Raza Abidi and Samina Raza Abidi},

url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6990486&isnumber=7369993},

doi = {10.1109/JBHI.2014.2383840},

issn = {2168-2194},

year  = {2016},

date = {2016-01-01},

journal = {IEEE Journal of Biomedical and Health Informatics},

volume = {20},

number = {1},

pages = {388-398},

abstract = {Computerizing paper-based CPG and then executing them can provide evidence-informed decision support to physicians at the point of care. Semantic web technologies especially Web Ontology Language (OWL) ontologies have been profusely used to represent computerized CPG. Using semantic web reasoning capabilities to execute OWL based computerized CPG unties them from a specific custom-built CPG execution engine and increases their shareability as any OWL reasoner and triple store can be utilized for CPG execution. However, existing semantic web reasoning based CPG execution engines suffer from lack of ability to execute CPG with high levels of expressivity, high cognitive load of computerization of paper-based CPG and updating their computerized versions. In order to address these limitations, we have developed three CPG execution engines based on OWL 1 DL, OWL 2 DL and OWL 2 DL + Semantic Web Rule Language (SWRL). OWL 1 DL serves as the base execution engine capable of executing a wide range of CPG constructs, however for executing highly complex CPG the OWL 2 DL and OWL 2 DL + SWRL offer additional executional capabilities. We evaluated the technical performance and medical correctness of our execution engines using a range of CPG. Technical evaluations show the efficiency of our CPG execution engines in terms of CPU time and validity of the generated recommendation in comparison to existing CPG execution engines. Medical evaluations by domain experts show the validity of the CPG-mediated therapy plans in terms of relevance, safety, and ordering for a wide range of patient scenarios.},

keywords = {Clinical Decision Support Systems, Clinical Practice Guidelines, OWL, Semantic Web},

pubstate = {published},

tppubtype = {article}

}