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Towards Understanding ICU Procedures using Similarities in Patient Trajectories: An exploratory study on the MIMIC-III intensive care database
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Recent advancements in Artificial Intelligence has prompted a shearexplosion of new research initiatives and applications, improving notonly existing technologies, but also opening up opportunities for newand exiting applications. This thesis explores the MIMIC-III intensive care unit database and conducts experiment on an interpretable feature space based on sever-ty scores, defining a patient health state, commonly used to predict mortality in an ICU setting. Patient health state trajectories are clustered and correlated with administered medication and performed procedures to get a better understanding of the potential usefulness in evaluating treatments on their effect on said health state, where commonalities and deviations in treatment can be understood. Furthermore, medication and procedure classification is carried out to explore their predictability using the severity subscore feature space.

Place, publisher, year, edition, pages
2018. , p. 124
Keywords [en]
ICU, Severity Scores, Patient Clustering, Mortality, Health State, Patient Trajectory, Clustering, Unsupervised learning, Classification, Data Mining, AI
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hh:diva-37416OAI: oai:DiVA.org:hh-37416DiVA, id: diva2:1229433
Subject / course
Computer science and engineering
Educational program
Master's Programme in Embedded and Intelligent Systems, 120 credits
Supervisors
Examiners
Available from: 2018-07-02 Created: 2018-06-29 Last updated: 2018-07-02Bibliographically approved

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fulltext(5888 kB)35 downloads
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File name FULLTEXT02.pdfFile size 5888 kBChecksum SHA-512
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Type fulltextMimetype application/pdf

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CAISR - Center for Applied Intelligent Systems Research
Engineering and Technology

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf