Cluster Analysis Reveals Subgroups with Different Risk Profiles and Sickness Absence Patterns in an Occupational Health Cohort.

Journal: Journal of occupational rehabilitation
Published Date: Jul 29, 2025

Abstract

PURPOSE: Using unsupervised and supervised machine learning methods, we aimed to identify clinically relevant groups of employees with similar characteristics and analyze the association of long and short sickness absence periods with these groups.

Authors

  • Anniina Anttila
    Tampere University, Arvo Ylpön Katu 34Tampereen Yliopisto, PL 100, 33014, Tampere, Finland. anniina.anttila@finla.fi.
  • Mikko Nuutinen
    Nordic Healthcare Group, Helsinki, Finland.
  • Riikka-Leena Leskelä
    Department of Public Health, University of Helsinki, Helsinki, Finland; Nordic Healthcare Group, Helsinki, Finland.
  • Mark van Gils
    Faculty of Medicine and Health Technology, Tampere University, Seinäjoki, Finland. Electronic address: mark.vangils@tuni.fi.
  • Anu Pekki
    Finla Työterveys, PL 42, 33211, Tampere, Finland.
  • Riitta Sauni
    Tampere University, Arvo Ylpön Katu 34Tampereen Yliopisto, PL 100, 33014, Tampere, Finland.

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