Execution-time integration of clinical practice guidelines to provide decision support for comorbid conditions

Borna Jafarpour, Samina Raza Abidi, William Van Woensel, Syed Sibte Raza Abidi

Résultat de recherche: Articleexamen par les pairs

20 Citations (Scopus)

Résumé

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.

Langue d'origineEnglish
Pages (de-à)117-137
Nombre de pages21
JournalArtificial Intelligence in Medicine
Volume94
DOI
Statut de publicationPublished - mars 2019

Note bibliographique

Publisher Copyright:
© 2019 Elsevier B.V.

ASJC Scopus Subject Areas

  • Medicine (miscellaneous)
  • Artificial Intelligence

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Citer

Jafarpour, B., Raza Abidi, S., Van Woensel, W., & Raza Abidi, S. S. (2019). Execution-time integration of clinical practice guidelines to provide decision support for comorbid conditions. Artificial Intelligence in Medicine, 94, 117-137. https://doi.org/10.1016/j.artmed.2019.02.003