Mitplan

A planning approach to mitigating concurrently applied clinical practice guidelines

Martin Michalowski, Szymon Wilk, Wojtek Michalowski, Marc Carrier

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

Abstract

As the overall population ages, patient complexity and the scope of their care is increasing. Over 60% of the population over 65 years of age suffers from multi-morbidity, which is associated with over two times as many patient-physician encounters. Yet clinical practice guidelines (CPGs) are developed to treat a single disease. To reconcile these two competing issues, we developed a framework for identifying and addressing adverse interactions in multi-morbid patients managed according to multiple CPGs. The framework relies on first-order logic (FOL) to represent CPGs and secondary medical knowledge and FOL theorem proving to establish valid patient management scenarios. In this work, we leverage the framework’s representation capabilities to simplify its mitigation process and cast it as a planning problem represented using the Planning Domain Definition Language (PDDL). We demonstrate the framework’s ability to identify and mitigate adverse interactions using planning actions, add support for durative clinical actions, and show the improved interpretability of management plan recommendations in the context of both proof-of-concept and clinical examples.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings
EditorsSzymon Wilk, Annette ten Teije, David Riaño
PublisherSpringer- Verlag
Pages93-103
Number of pages11
ISBN (Print)9783030216412
DOIs
StatePublished - Jan 1 2019
Event17th Conference on Artificial Intelligence in Medicine, AIME 2019 - Poznan, Poland
Duration: Jun 26 2019Jun 29 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11526 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th Conference on Artificial Intelligence in Medicine, AIME 2019
CountryPoland
CityPoznan
Period6/26/196/29/19

Fingerprint

Planning
First-order Logic
Theorem proving
Morbidity
Theorem Proving
Interpretability
Interaction
Leverage
Recommendations
Simplify
Valid
Scenarios
Framework
Demonstrate

Keywords

  • Clinical practice guidelines
  • Multi-morbidity
  • Planning

Cite this

Michalowski, M., Wilk, S., Michalowski, W., & Carrier, M. (2019). Mitplan: A planning approach to mitigating concurrently applied clinical practice guidelines. In S. Wilk, A. ten Teije, & D. Riaño (Eds.), Artificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings (pp. 93-103). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11526 LNAI). Springer- Verlag. https://doi.org/10.1007/978-3-030-21642-9_13

Mitplan : A planning approach to mitigating concurrently applied clinical practice guidelines. / Michalowski, Martin; Wilk, Szymon; Michalowski, Wojtek; Carrier, Marc.

Artificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings. ed. / Szymon Wilk; Annette ten Teije; David Riaño. Springer- Verlag, 2019. p. 93-103 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11526 LNAI).

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

Michalowski, M, Wilk, S, Michalowski, W & Carrier, M 2019, Mitplan: A planning approach to mitigating concurrently applied clinical practice guidelines. in S Wilk, A ten Teije & D Riaño (eds), Artificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11526 LNAI, Springer- Verlag, pp. 93-103, 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, 6/26/19. https://doi.org/10.1007/978-3-030-21642-9_13
Michalowski M, Wilk S, Michalowski W, Carrier M. Mitplan: A planning approach to mitigating concurrently applied clinical practice guidelines. In Wilk S, ten Teije A, Riaño D, editors, Artificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings. Springer- Verlag. 2019. p. 93-103. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-21642-9_13
Michalowski, Martin ; Wilk, Szymon ; Michalowski, Wojtek ; Carrier, Marc. / Mitplan : A planning approach to mitigating concurrently applied clinical practice guidelines. Artificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings. editor / Szymon Wilk ; Annette ten Teije ; David Riaño. Springer- Verlag, 2019. pp. 93-103 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{9f56592cb430408598d09cb0ef764e40,
title = "Mitplan: A planning approach to mitigating concurrently applied clinical practice guidelines",
abstract = "As the overall population ages, patient complexity and the scope of their care is increasing. Over 60{\%} of the population over 65 years of age suffers from multi-morbidity, which is associated with over two times as many patient-physician encounters. Yet clinical practice guidelines (CPGs) are developed to treat a single disease. To reconcile these two competing issues, we developed a framework for identifying and addressing adverse interactions in multi-morbid patients managed according to multiple CPGs. The framework relies on first-order logic (FOL) to represent CPGs and secondary medical knowledge and FOL theorem proving to establish valid patient management scenarios. In this work, we leverage the framework’s representation capabilities to simplify its mitigation process and cast it as a planning problem represented using the Planning Domain Definition Language (PDDL). We demonstrate the framework’s ability to identify and mitigate adverse interactions using planning actions, add support for durative clinical actions, and show the improved interpretability of management plan recommendations in the context of both proof-of-concept and clinical examples.",
keywords = "Clinical practice guidelines, Multi-morbidity, Planning",
author = "Martin Michalowski and Szymon Wilk and Wojtek Michalowski and Marc Carrier",
year = "2019",
month = "1",
day = "1",
doi = "10.1007/978-3-030-21642-9_13",
language = "English (US)",
isbn = "9783030216412",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer- Verlag",
pages = "93--103",
editor = "Szymon Wilk and {ten Teije}, Annette and David Ria{\~n}o",
booktitle = "Artificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings",

}

TY - GEN

T1 - Mitplan

T2 - A planning approach to mitigating concurrently applied clinical practice guidelines

AU - Michalowski, Martin

AU - Wilk, Szymon

AU - Michalowski, Wojtek

AU - Carrier, Marc

PY - 2019/1/1

Y1 - 2019/1/1

N2 - As the overall population ages, patient complexity and the scope of their care is increasing. Over 60% of the population over 65 years of age suffers from multi-morbidity, which is associated with over two times as many patient-physician encounters. Yet clinical practice guidelines (CPGs) are developed to treat a single disease. To reconcile these two competing issues, we developed a framework for identifying and addressing adverse interactions in multi-morbid patients managed according to multiple CPGs. The framework relies on first-order logic (FOL) to represent CPGs and secondary medical knowledge and FOL theorem proving to establish valid patient management scenarios. In this work, we leverage the framework’s representation capabilities to simplify its mitigation process and cast it as a planning problem represented using the Planning Domain Definition Language (PDDL). We demonstrate the framework’s ability to identify and mitigate adverse interactions using planning actions, add support for durative clinical actions, and show the improved interpretability of management plan recommendations in the context of both proof-of-concept and clinical examples.

AB - As the overall population ages, patient complexity and the scope of their care is increasing. Over 60% of the population over 65 years of age suffers from multi-morbidity, which is associated with over two times as many patient-physician encounters. Yet clinical practice guidelines (CPGs) are developed to treat a single disease. To reconcile these two competing issues, we developed a framework for identifying and addressing adverse interactions in multi-morbid patients managed according to multiple CPGs. The framework relies on first-order logic (FOL) to represent CPGs and secondary medical knowledge and FOL theorem proving to establish valid patient management scenarios. In this work, we leverage the framework’s representation capabilities to simplify its mitigation process and cast it as a planning problem represented using the Planning Domain Definition Language (PDDL). We demonstrate the framework’s ability to identify and mitigate adverse interactions using planning actions, add support for durative clinical actions, and show the improved interpretability of management plan recommendations in the context of both proof-of-concept and clinical examples.

KW - Clinical practice guidelines

KW - Multi-morbidity

KW - Planning

UR - http://www.scopus.com/inward/record.url?scp=85068339861&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85068339861&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-21642-9_13

DO - 10.1007/978-3-030-21642-9_13

M3 - Conference contribution

SN - 9783030216412

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 93

EP - 103

BT - Artificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings

A2 - Wilk, Szymon

A2 - ten Teije, Annette

A2 - Riaño, David

PB - Springer- Verlag

ER -