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Towards Understanding ICU Treatments Using Patient Health Trajectories
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-7453-9186
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-7796-5201
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-3495-2961
2019 (Engelska)Ingår i: Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems: Revised Selected Papers, Heidelberg: Springer, 2019, s. 67-81Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Heidelberg: Springer, 2019. s. 67-81
Serie
Lecture Notes in Computer Science, ISSN 1611-3349
Nyckelord [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
Nationell ämneskategori
Hälso- och sjukvårdsorganisation, hälsopolitik och hälsoekonomi Datavetenskap (datalogi)
Identifikatorer
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 (tryckt)ISBN: 978-3-030-37446-4 (digital)OAI: oai:DiVA.org:hh-41538DiVA, id: diva2:1391301
Konferens
AIME 2019 International Workshops, KR4HC/ProHealth and TEAAM, Poznan, Poland, June 26–29, 2019
Tillgänglig från: 2020-02-04 Skapad: 2020-02-04 Senast uppdaterad: 2023-08-21Bibliografiskt granskad

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Galozy, AlexanderNowaczyk, SławomirPinheiro Sant'Anna, Anita

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Galozy, AlexanderNowaczyk, SławomirPinheiro Sant'Anna, Anita
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CAISR Centrum för tillämpade intelligenta system (IS-lab)
Hälso- och sjukvårdsorganisation, hälsopolitik och hälsoekonomiDatavetenskap (datalogi)

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