Predicting workplace absenteeism using machine learning: a pilot study in occupational health

This pilot study demonstrates the feasibility of machine learning approaches for occupational health management by enabling identification of employees at risk for prolonged absenteeism. While showing promise for supporting personalized health interventions and resource allocation, implementation requires external validation across multiple organizations and careful consideration of ethical implications regarding employee privacy and algorithmic fairness.

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Publication Details

Journal
Journal of Occupational Medicine and Toxicology
Published
2025-11-10
DOI
https://doi.org/10.1186/s12995-025-00482-5
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Predicting workplace absenteeism using machine learning: a pilot study in occupational health

Pablo Llamas Blázquez
Journal of Occupational Medicine and Toxicology
Artificial Intelligence in Healthcare and Education
article

Predicting workplace absenteeism using machine learning: a pilot study in occupational health

Pablo Llamas Blázquez
article en

Abstract

This pilot study demonstrates the feasibility of machine learning approaches for occupational health management by enabling identification of employees at risk for prolonged absenteeism. While showing promise for supporting personalized health interventions and resource allocation, implementation requires external validation across multiple organizations and careful consideration of ethical implications regarding employee privacy and algorithmic fairness.

Journal of Occupational Medicine and ToxicologyVol. 20(1)
Instituto Cajal (ES), Hospital Universitario Ramón y Cajal (ES)
Openalex Percentile: Top 74%
Artificial Intelligence in Healthcare and Education
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