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Clustering Diagnostic Profiles of Patients
Aalto University, Finland.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2019 (English)In: Artificial Intelligence Applications and Innovations: Proceedings / [ed] John MacIntyre, Ilias Maglogiannis, Lazaros Iliadis, Elias Pimenidis, Springer, 2019, p. 120-126Conference paper, Published paper (Refereed)
Abstract [en]

Electronic Health Records provide a wealth of information about the care of patients and can be used for checking the conformity of planned care, computing statistics of disease prevalence, or predicting diagnoses based on observed symptoms, for instance. In this paper, we explore and analyze the recorded diagnoses of patients in a hospital database in retrospect, in order to derive profiles of diagnoses in the patient database. We develop a data representation compatible with a clustering approach and present our clustering approach to perform the exploration. We use a k-means clustering model for identifying groups in our binary vector representation of diagnoses and present appropriate model selection techniques to select the number of clusters. Furthermore, we discuss possibilities for interpretation in terms of diagnosis probabilities, in the light of external variables and with the common diagnoses occurring together.

Place, publisher, year, edition, pages
Springer, 2019. p. 120-126
Series
IFIP Advances in Information and Communication Technology, ISSN 1868-4238, E-ISSN 1868-422X ; 559
Keywords [en]
Medical records, Binary representations, Clustering
National Category
Computer Sciences
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-177141DOI: 10.1007/978-3-030-19823-7_9ISBN: 978-3-030-19822-0 (print)ISBN: 978-3-030-19823-7 (electronic)OAI: oai:DiVA.org:su-177141DiVA, id: diva2:1379861
Conference
15th IFIP WG 12.5 International Conference, AIAI 2019, Hersonissos, Crete, Greece, May 24–26, 2019
Available from: 2019-12-17 Created: 2019-12-17 Last updated: 2019-12-20Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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Output format
  • html
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  • asciidoc
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