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Development of an artificial intelligence model to estimate psychiatrist-assessed mental health-related presenteeism

Objectives This study aimed to contribute to the development of an AI-based system that supports worker health and productivity by enabling early detection of presenteeism. We tested whether an AI model could assess mental health-related presenteeism with accuracy comparable to that of psychiatrists and whether the frequency of application use was comparable between avatar-based and real-person interfaces. Methods This study aimed to design a multivariable prediction model among white-collar employees in a Japan…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-1161· cite & verify
Development of an artificial intelligence model to estimate psychiatrist-assessed mental health-related presenteeism
Student Counseling Sercvices Reception - Texas A&M by Patrick Creighton. CC BY-SA 4.0 · https://creativecommons.org/licenses/by-sa/4.0

In brief

Objectives This study aimed to contribute to the development of an AI-based system that supports worker health and productivity by enabling early detection of presenteeism. We tested whether an AI model could assess mental health-related presenteeism with accuracy comparable to that of psychiatrists and whether the frequency of application use was comparable between avatar-based and real-person interfaces.

The primary outcome measure was the accuracy of the AI model in estimating workers' mental health-related presenteeism. Results The AI model achieved an overall accuracy of 72.2%, a macro F1 score of 0.64, and a weighted F1 score of 0.73 compared with psychiatrists' ratings.

Main points

  1. Objectives This study aimed to contribute to the development of an AI-based system that supports worker health and productivity by enabling early detection of presenteeism.
  2. We tested whether an AI model could assess mental health-related presenteeism with accuracy comparable to that of psychiatrists and whether the frequency of application use was comparable between avatar-based and real-person interfaces.
  3. Methods This study aimed to design a multivariable prediction model among white-collar employees in a Japanese company.

The gain

Results The AI model achieved an overall accuracy of 72.2%, a macro F1 score of 0.64, and a weighted F1 score of 0.73 compared with psychiatrists' ratings.

The rundown

Methods This study aimed to design a multivariable prediction model among white-collar employees in a Japanese company. The participants comprised 117 white-collar workers who provided a total of 1,631 video responses to a standardized health-status question over a period of 10 working days.

Sources

  1. Peer-reviewedJournal of Occupational Health2026-09-21

The debate