Explainable Decision Support Using Task Network Models in Notation3: Computerizing Lipid Management Clinical Guidelines as Interactive Task Networks

William Van Woensel, Samina Abidi, Karthik Tennankore, George Worthen, Syed Sibte Raza Abidi

Producción científica: Capítulo en Libro/Reporte/Acta de conferenciaContribución a la conferencia

6 Citas (Scopus)

Resumen

Knowledge-driven Clinical Decision Support (CDS) involves the computerization of paper-based clinical guidelines to issue evidence-based recommendations at points-of-care. The computerization of such guidelines in terms of a Task Network Model (TNM) conveniently models them as intuitive workflow models, which can be executed against patient health profiles. We present the GLEAN model that encodes an extensible Finite State Machine (FSM) executional semantics for modular TNM. Extensibility is provided in terms of a high-level formalism for defining execution semantics of custom TNM constructs. GLEAN is implemented using the Notation3 Semantic Web language, which provides powerful features for decisional criteria and queries, and offers integration with the HL7 FHIR standard. We explain CIG workflows as visual, intuitive workflow diagrams that are guided by a concrete patient profile at runtime. As a use case, we computerized guidelines on lipid management for Chronic Kidney Disease (CKD), a challenging problem for many Primary Care Providers (PCPs). To educate PCP on lipid management for CKD, we leverage GLEAN’s easy modularization of CIG and CIG explanations as visual runtime workflows.

Idioma originalEnglish
Título de la publicación alojadaArtificial Intelligence in Medicine - 20th International Conference on Artificial Intelligence in Medicine, AIME 2022, Proceedings
EditoresMartin Michalowski, Syed Sibte Raza Abidi, Samina Abidi
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas3-13
Número de páginas11
ISBN (versión impresa)9783031093418
DOI
EstadoPublished - 2022
Evento20th International Conference on Artificial Intelligence in Medicine, AIME 2022 - Halifax, Canada
Duración: jun. 14 2022jun. 17 2022

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen13263 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conference

Conference20th International Conference on Artificial Intelligence in Medicine, AIME 2022
País/TerritorioCanada
CiudadHalifax
Período6/14/226/17/22

Nota bibliográfica

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

ASJC Scopus Subject Areas

  • Theoretical Computer Science
  • General Computer Science

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