Development of an artificial intelligence model to estimate psychiatrist-assessed mental health-related presenteeism
Shotaro Doki, Masakazu Hirokawa, Taiga Noguchi, Daisuke Hori, Soma Nishimura, Katsuya Hotta, Yuya Iwata, Naoko Kouda, Shota Matsumoto, Kenji Suzuki, Shin-ichiro SasaharaAbstract
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 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. The primary outcome measure was the accuracy of the AI model in estimating workers’ mental health–related presenteeism. The secondary outcome was the frequency of application use when inquiring about workers’ health conditions, comparing the avatar-based interface with the real-person interface.
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. Agreement between the two psychiatrists was 84.5%. Participants were allocated to either an avatar-based interface or a real-person interface, with no significant differences observed between groups in baseline characteristics or frequency of application use, whereas a significant difference was observed in the frequency of missing values.
Conclusions
The developed AI model demonstrated performance comparable to psychiatrists in estimating mental health–related presenteeism from video data. This approach offers a novel, objective alternative to traditional questionnaire-based methods.