Machine Learning Models to Identify Individuals With Imminent Suicide Risk Using a Wearable Device: A Pilot Study

• 발행기관: 대한신경정신의학회
• 저자명: Jumyung Um Jongsu Park Dong Eun Lee Jae Eun Ahn Ji Hyun Baek
• 간행물 정보: 『Psychiatry Investigation』제22권 제2호, 156~166쪽, 전체 11쪽
• 주제분류: 의약학 > 정신과학
• 파일형태: PDF
• 발행일자: 2025.02.17

국문 초록

Objective We aimed to determine whether individuals at immediate risk of suicide could be identified using data from a commercially available wearable device. Methods Thirty-nine participants experiencing acute depressive episodes and 20 age- and sex-matched healthy controls wore a commercially available wearable device (Galaxy Watch Active2) for two months. We collected data on activities, sleep, and physiological metrics like heart rate and heart rate variability using the wearable device. Participants rated their mood spontaneously twice daily on a Likert scale displayed on the device. Mood ratings by clinicians were performed at weeks 0, 2, 4, and 8. The suicide risk was assessed using the Hamilton Depression Rating Scale’s suicide item score (HAMD-3). We developed two predictive models using machine learning: a single-level model that processed all data simultaneously to identify those at immediate suicide risk (HAMD-3 scores ≥1) and a multi-level model. We compared the predictions of imminent suicide risk from both models. Results Both the single-step and multi-step models effectively predicted imminent suicide risk. The multi-step model outperformed the single-step model in predicting imminent suicide risk with area under the curve scores of 0.89 compared to 0.88. In the multi-step model, the HAMD total score and heart rate variability were most significant, whereas in the single-step model, the HAMD total score and diagnosis were key predictors. Conclusion Wearable devices are a promising tool for identifying individuals at immediate risk of suicide. Future research with more refined temporal resolution is recommended.

목차

INTRODUCTION

METHODS

RESULTS

DISCUSSION

REFERENCES

키워드

Suicide Depression Daily mood monitoring Imminent suicide risk Wearable device Risk monitoring