aceDevelopment and internal validation of a machine learning model for work-related sick leave in Brazilian workers with mental disorders
Background: Mental disorders are a leading cause of work-related sick leave in Brazil, yet routinely collected surveillance data are seldom used to build prediction tools for occupational health services. We aimed to develop and internally validate machine learning models predicting work-related sick leave among individuals notified with work-related mental disorders. Methods: We analyzed 21,601 notifications of work-related mental disorder (ICD-10 F99) from Brazil's national surveillance system (SINAN, 2006-202…
Models trained on routine national surveillance notifications gave moderate-discrimination prediction of sick leave, with positive net benefit across roughly 30-70% decision thresholds.
The champion model discriminated markedly worse for Black workers and workers with secondary education, so deployment would need recalibration and fairness auditing.
Only internal validation was performed, so the model is not ready for deployment and needs external, prospective validation, plus recalibration and fairness auditing for disadvantaged subgroups.
Evidence
- Peer-reviewedInternational Journal of Medical Informatics2026-09-23
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Truvace Impact Record TRV-2026-1528, v1: “Development and internal validation of a machine learning model for work-related sick leave in Brazilian workers with mental disorders.” Truvace, 2026-10-16. /record/TRV-2026-1528 (accessed at citation time). sha256 5966b6b792b5b7dc…
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