MitPlan 2.0: Enhanced Support for Multi-morbid Patient Management Using Planning

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


The complexity of patient care is growing due to an ageing population. As chronic illnesses become more common, the incidence of multi-morbidity increases. Generating disease management plans for multi-morbid patients requires the integration of multiple evidence-based interventions, represented as clinical practice guidelines (CPGs), that are designed to treat a single condition. Our previous work developed a mitigation framework called MitPlan that represented the generation of treatment as a planning problem. The framework used the Planning Domain Definition Language (PDDL) to represent clinical and patient information needed to identify and mitigate adverse interactions resulting from the concurrent application of multiple CPGs for a given patient encounter. In this paper we describe MitPlan 2.0 that supports shared decision-making by identifying a treatment plan optimized according to patient preferences, treatment cost, or perceived patient’s adherence to medication. It mitigates adverse interactions using planning constructs, eliminating the need for procedural handling of adverse interactions and as such provides flexible and comprehensive decision support at the point of care. We demonstrate MitPlan 2.0’s extended capabilities using synthetic scenarios approximating real-world clinical use cases and demonstrate its new capabilities within the context of atrial fibrillation.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Medicine - 19th International Conference on Artificial Intelligence in Medicine, AIME 2021, Proceedings
EditorsAllan Tucker, Pedro Henriques Abreu, Jaime Cardoso, Pedro Pereira Rodrigues, David Riaño
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783030772109
StatePublished - Jun 8 2021
Event19th International Conference on Artificial Intelligence in Medicine, AIME 2021 - Virtual, Online
Duration: Jun 15 2021Jun 18 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12721 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th International Conference on Artificial Intelligence in Medicine, AIME 2021
CityVirtual, Online

Bibliographical note

Funding Information:
We thank Andrew Coles and Amanda Coles for their clarifications regarding PDDL and OPTIC and the reviewers for their helpful comments.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


  • Clinical practice guidelines
  • Multi-morbidity
  • Planning


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