This paper is in the following e-collection/theme issue:

Secondary Use of Clinical Data for Research and Surveillance (412) Quality Improvement (274) Electronic Health Records (1164) eHealth Infrastructures (387) Tools, Programs and Algorithms (317)

Published on in Vol 10, No 4 (2022): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34274, first published .
Exploring Patient Multimorbidity and Complexity Using Health Insurance Claims Data: A Cluster Analysis Approach

Exploring Patient Multimorbidity and Complexity Using Health Insurance Claims Data: A Cluster Analysis Approach

Exploring Patient Multimorbidity and Complexity Using Health Insurance Claims Data: A Cluster Analysis Approach

Authors of this article:

Anna Nicolet1 Author Orcid Image ;   Dan Assouline1 Author Orcid Image ;   Marie-Annick Le Pogam1 Author Orcid Image ;   Clémence Perraudin1 Author Orcid Image ;   Christophe Bagnoud2 Author Orcid Image ;   Joël Wagner3 Author Orcid Image ;   Joachim Marti1 Author Orcid Image ;   Isabelle Peytremann-Bridevaux1 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

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  1. Beuken M, Kanera I, Ezendam N, Braun S, Zoet M. Identification and Potential Use of Clusters of Patients With Colorectal Cancer and Patients With Prostate Cancer in Clinical Practice: Explorative Mixed Methods Study. JMIR Cancer 2022;8(4):e42908 View
  2. Boussina A, Wardi G, Shashikumar S, Malhotra A, Zheng K, Nemati S. Representation Learning and Spectral Clustering for the Development and External Validation of Dynamic Sepsis Phenotypes: Observational Cohort Study. Journal of Medical Internet Research 2023;25:e45614 View
  3. Beuken M, Kleynen M, Braun S, Van Berkel K, van der Kallen C, Koster A, Bosma H, Berendschot T, Houben A, Dukers-Muijrers N, van den Bergh J, Kroon A, Kanera I. Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study. JMIR Medical Informatics 2025;13:e64479 View