A Goal-Oriented Methodology for Treatment of Patients with Multimorbidity - Goal Comorbidities (GoCom) Proof-of-Concept Demonstration

Alexandra Kogan, Mor Peleg, Samson W. Tu, Raviv Allon, Natanel Khaitov, Irit Hochberg

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

Abstract

Advancement in medicine has increased the average population age, however, physicians are still burdened with the complexity of treatment of multimorbidity patients due to many potential interactions among the patient’s medications, and diseases. We developed a goal-oriented methodology for management of multimorbidity patients called GoCom (for Goal Comorbidities). GoCom’s aim is to help manage the patient’s changing health state that may prompt new goals to arise. GoCom utilizes computer-interpretable clinical guidelines formalized using the PROforma representation. The guidelines are modeled according to a previously published guide on modeling goal-oriented, metaproperty enriched tasks in PROforma. The tasks are retrieved by the main algorithm of the system named the “Controller” that creates a hierarchical goal-oriented tree structure that is personalized for the patient according to their specific data. Tree structures are created for all of the patient’s problems and are formed as a patient forest. The Controller behavioral patterns reason over the patient data and create clinically-valid solutions that are presented to the physician with generated explanations. We evaluated GoCom for correctness and completeness with complex multimorbidity case studies. The first evaluation was a pilot study with ten 6th year medical students and the second evaluation was with 27 6th year medical students and interns. Use of GoCom increased completeness and correctness and the explanations and visualization were viewed as useful by the participants.

Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine - 20th International Conference on Artificial Intelligence in Medicine, AIME 2022, Proceedings
EditorsMartin Michalowski, Syed Sibte Raza Abidi, Samina Abidi
PublisherSpringer Science and Business Media Deutschland GmbH
Pages426-430
Number of pages5
ISBN (Print)9783031093418
DOIs
StatePublished - 2022
Event20th International Conference on Artificial Intelligence in Medicine, AIME 2022 - Halifax, Canada
Duration: 14 Jun 202217 Jun 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13263 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Artificial Intelligence in Medicine, AIME 2022
Country/TerritoryCanada
CityHalifax
Period14/06/2217/06/22

Bibliographical note

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

Keywords

  • Comorbidity
  • Computer-interpretable guidelines
  • Decision-support
  • Multimorbidity

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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