Using graph rewriting to operationalize medical knowledge for the revision of concurrently applied clinical practice guidelines

Martin Michalowski, Malvika Rao, Szymon Wilk, Wojtek Michalowski, Marc Carrier

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Clinical practice guidelines (CPGs) are patient management tools that synthesize medical knowledge into an actionable format. CPGs are disease specific with limited applicability to the management of complex patients suffering from multimorbidity. For the management of these patients, CPGs need to be augmented with secondary medical knowledge coming from a variety of knowledge repositories. The operationalization of this knowledge is key to increasing CPGs’ uptake in clinical practice. In this work, we propose an approach to operationalizing secondary medical knowledge inspired by graph rewriting. We assume that the CPGs can be represented as task network models, and provide an approach for representing and applying codified medical knowledge to a specific patient encounter. We formally define revisions that model and mitigate adverse interactions between CPGs and we use a vocabulary of terms to instantiate these revisions. We demonstrate the application of our approach using synthetic and clinical examples. We conclude by identifying areas for future work with the vision of developing a theory of mitigation that will facilitate the development of comprehensive decision support for the management of multimorbid patients.

Original languageEnglish (US)
Article number102550
JournalArtificial Intelligence in Medicine
Volume140
DOIs
StatePublished - Jun 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.

Keywords

  • Automated planning
  • Clinical practice guidelines
  • Graph rewriting
  • Multi-morbidity

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't

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