A CIG Integration Framework to Provide Decision Support for Comorbid Conditions Using Transaction-Based Semantics and Temporal Planning

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

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

3 Citas (Scopus)

Resumen

Managing comorbid conditions, i.e., patients with multiple medical conditions, is quite challenging for Clinical Decision Support Systems (CDSS) based on computerized Clinical Practice Guidelines (CPG). In case of comorbidity, CDSS will need to recommend treatments from multiple different CPG, which may adversely interact (e.g., drug-disease interactions), or introduce inefficiencies. A-priori, static integration of computerized comorbid CPG is insufficient for clinical practice. In this paper, we present a solution for dynamic integration of CPG in response to evolving health profiles. Using Description and Transaction Logics, we define a set of CIG integration semantics for encoding integration decisions that cope with comorbidity issues at execution-time. These dynamic, transaction-based semantics are well-suited to roll back prior decisions when no longer safe or efficient; or, inversely, apply new decisions when relevant. Moreover, comorbid CIG integration should consider temporal properties of CIG tasks—at execution-time, these properties will be influenced by a range of temporal constraints. Given all temporal constraints, optimal task schedules will be calculated that will determine the feasibility of CIG integration decisions.

Idioma originalEnglish
Título de la publicación alojadaArtificial Intelligence in Medicine - 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings
EditoresMartin Michalowski, Robert Moskovitch
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas440-450
Número de páginas11
ISBN (versión impresa)9783030591366
DOI
EstadoPublished - 2020
Evento18th International Conference on Artificial Intelligence in Medicine, AIME 2020 - Minneapolis, United States
Duración: ago. 25 2020ago. 28 2020

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen12299 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conference

Conference18th International Conference on Artificial Intelligence in Medicine, AIME 2020
País/TerritorioUnited States
CiudadMinneapolis
Período8/25/208/28/20

Nota bibliográfica

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

ASJC Scopus Subject Areas

  • Theoretical Computer Science
  • General Computer Science

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