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Towards Understanding ICU Treatments Using Patient Health Trajectories
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-7453-9186
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-7796-5201
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-3495-2961
2019 (English)In: Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems: Revised Selected Papers, Heidelberg: Springer, 2019, p. 67-81Conference paper, Published paper (Refereed)
Abstract [en]

Overtreatment or mistreatment of patients is a phenomenon commonly encountered in health care and especially in the Intensive Care Unit (ICU) resulting in increased morbidity and mortality. We explore the MIMIC-III intensive care unit database and conduct experiments on an interpretable feature space based on the fusion of severity subscores, commonly used to predict mortality in an ICU setting. Clustering of medication and procedure context vectors based on a semantic representation has been performed to find common and individual treatment patterns. Two-day patient health state trajectories of a cohort of congestive heart failure patients are clustered and correlated with the treatment and evaluated based on an increase or reduction of probability of mortality on the second day of stay. Experimental results show differences in treatments and outcomes and the potential for using patient health state trajectories as a starting point for further evaluation of medical treatments and interventions. © Springer Nature Switzerland AG 2019.

Place, publisher, year, edition, pages
Heidelberg: Springer, 2019. p. 67-81
Series
Lecture Notes in Computer Science, ISSN 1611-3349
Keywords [en]
Clustering, Electronic Health Records, Health trajectory, Intensive care treatments, Knowledge representation, Medical computing, Medical information systems, Patient treatment, Semantics, Trajectories, Congestive heart failures, Context vector, Electronic health record, Intensive care, Medical treatment, Patient health, Semantic representation, Intensive care units
National Category
Health Care Service and Management, Health Policy and Services and Health Economy Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-41538DOI: 10.1007/978-3-030-37446-4_6ISI: 000654170100006Scopus ID: 2-s2.0-85078449305ISBN: 978-3-030-37445-7 (print)ISBN: 978-3-030-37446-4 (electronic)OAI: oai:DiVA.org:hh-41538DiVA, id: diva2:1391301
Conference
AIME 2019 International Workshops, KR4HC/ProHealth and TEAAM, Poznan, Poland, June 26–29, 2019
Available from: 2020-02-04 Created: 2020-02-04 Last updated: 2023-08-21Bibliographically approved

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Galozy, AlexanderNowaczyk, SławomirPinheiro Sant'Anna, Anita

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