Identifying risk factors of long sickness absences: a registry-based study using explainable AI methods.

ABSTRACT:

Objective

To identify and explore variable groups and individual predictors of long sickness absences outside of well-known predictors such as service use and previous sickness absence using machine learning, explainable artificial intelligence methods and a submodel approach.

Design

Retrospective study of prospectively collected registry data on sickness absences and a questionnaire used in health examinations.

Setting

Electronic medical record data of one large occupational health service provider in Finland.

Participants

11 533 employees of various occupations who, between 2011 and 2019, had at least once completed a health questionnaire that could be linked to service usage data and who had not had their initial health check within 1 year before or 3 months

SUBMITTER: Anttila A 

PROVIDER: S-EPMC12587995 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature