Clustering Diagnostic Profiles of Patients

Jaakko Hollmén, Panagiotis Papapetrou*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


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.

Original languageEnglish
Title of host publicationArtificial Intelligence Applications and Innovations - 15th IFIP WG 12.5 International Conference, AIAI 2019, Proceedings
EditorsIlias Maglogiannis, Elias Pimenidis, Lazaros Iliadis, John MacIntyre
Number of pages7
Publication statusPublished - 1 Jan 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Artificial Intelligence Applications and Innovations - Hersonissos, Greece
Duration: 24 May 201926 May 2019
Conference number: 15

Publication series

NameIFIP Advances in Information and Communication Technology
ISSN (Print)1868-4238


ConferenceInternational Conference on Artificial Intelligence Applications and Innovations
Abbreviated titleAIAI


  • Binary representations
  • Clustering
  • Medical records

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