Analyzing Recommendations Interactions in Clinical Guidelines: Impact of action type hierarchies and causation beliefs

Veruska Carretta Zamborlini, Marcos Da Silveira, Cedric Pruski, Annette ten Teije, Frank van Harmelen

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Accounting for patients with multiple health conditions is a complex task that requires analysing potential interactions among recommendations meant to address each condition. Although some approaches have been proposed to address this issue, important features still require more investigation, such as (re)usability and scalability. To this end, this paper presents an approach that relies on reusable rules for detecting interactions among recommendations coming from various guidelines. It extends previously proposed models by introducing the notions of action type hierarchy and causation beliefs, and provides a systematic analysis of relevant interactions in the context of multimorbidity. Finally, the approach is assessed based on a case-study taken from the literature to highlight the added value of the approach.
Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine - 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Proceedings
PublisherSpringer/Verlag
Pages317-326
Number of pages10
Volume9105
ISBN (Print)9783319195506
DOIs
Publication statusPublished - 2015
Event15th Conference on Artificial Intelligence in Medicine (AIME 2015) - Pavia, Italy
Duration: 17 Jun 201520 Jun 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9105
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th Conference on Artificial Intelligence in Medicine (AIME 2015)
CountryItaly
CityPavia
Period17/06/1520/06/15

Keywords

  • Clinical knowledge representation
  • Combining medical guidelines
  • Multimorbidity

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