Prediction of Behavioral Health Employee Turnover With HR Data–Based Machine Learning Combined With Job Well-Being Indicators

This study provides a viable and less labor-intensive method for identifying employees at high risk for turnover by applying ML models trained on historical HR data and identifies additional survey data that could augment predictions.

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Publication Details

Journal
Psychiatric Services
Published
2026-01-30
DOI
https://doi.org/10.1176/appi.ps.20250364
Primary Topic
AI and HR Technologies
Type
article
Field-Weighted Citation Impact
0.00
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article

Prediction of Behavioral Health Employee Turnover With HR Data–Based Machine Learning Combined With Job Well-Being Indicators

Wei Wu, Sadaaki Fukui
Psychiatric Services
AI and HR Technologies
article

Prediction of Behavioral Health Employee Turnover With HR Data–Based Machine Learning Combined With Job Well-Being Indicators

Wei Wu, Sadaaki Fukui
article en

Abstract

This study provides a viable and less labor-intensive method for identifying employees at high risk for turnover by applying ML models trained on historical HR data and identifies additional survey data that could augment predictions.

Psychiatric Services
University of Indianapolis (US), Indiana University Indianapolis (US), Indiana University – Purdue University Indianapolis (US)
Decent work and economic growth
Openalex Percentile: Top 73%
AI and HR Technologies
DOISourceOpenAlex
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