使用可穿戴设备识别具有即将自杀风险的个体的机器学习模型:一项试点研究。

IF 1.8 4区 医学 Q3 PSYCHIATRY
Psychiatry Investigation Pub Date : 2025-02-01 Epub Date: 2025-02-17 DOI:10.30773/pi.2024.0257
Jumyung Um, Jongsu Park, Dong Eun Lee, Jae Eun Ahn, Ji Hyun Baek
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引用次数: 0

摘要

目的:我们旨在确定是否可以使用市售可穿戴设备的数据来识别具有直接自杀风险的个体。方法:39名急性抑郁发作的参与者和20名年龄和性别匹配的健康对照者佩戴了一个市售的可穿戴设备(Galaxy Watch Active2)两个月。我们使用可穿戴设备收集活动、睡眠和心率和心率变异性等生理指标的数据。参与者每天两次根据设备上显示的李克特量表自发地评估自己的情绪。临床医生在第0、2、4和8周进行情绪评分。自杀风险评估采用汉密尔顿抑郁评定量表自杀项目得分(HAMD-3)。我们利用机器学习开发了两种预测模型:一种是单级模型,它同时处理所有数据,以识别那些有直接自杀风险的人(HAMD-3得分≥1);另一种是多级模型。我们比较了两种模型对即将发生的自杀风险的预测。结果:单步模型和多步模型均能有效预测即将发生自杀风险。多步骤模型在预测即将自杀风险方面优于单步骤模型,曲线下面积得分为0.89比0.88。在多步骤模型中,HAMD总分和心率变异性最显著,而在单步骤模型中,HAMD总分和诊断是关键预测因子。结论:可穿戴设备是一种很有前途的工具,可以用来识别有自杀风险的个体。建议未来进行更精细的时间分辨率研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Models to Identify Individuals With Imminent Suicide Risk Using a Wearable Device: A Pilot Study.

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 multilevel 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.

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来源期刊
CiteScore
4.10
自引率
3.70%
发文量
105
审稿时长
6-12 weeks
期刊介绍: The Psychiatry Investigation is published on the 25th day of every month in English by the Korean Neuropsychiatric Association (KNPA). The Journal covers the whole range of psychiatry and neuroscience. Both basic and clinical contributions are encouraged from all disciplines and research areas relevant to the pathophysiology and management of neuropsychiatric disorders and symptoms, as well as researches related to cross cultural psychiatry and ethnic issues in psychiatry. The Journal publishes editorials, review articles, original articles, brief reports, viewpoints and correspondences. All research articles are peer reviewed. Contributions are accepted for publication on the condition that their substance has not been published or submitted for publication elsewhere. Authors submitting papers to the Journal (serially or otherwise) with a common theme or using data derived from the same sample (or a subset thereof) must send details of all relevant previous publications and simultaneous submissions. The Journal is not responsible for statements made by contributors. Material in the Journal does not necessarily reflect the views of the Editor or of the KNPA. Manuscripts accepted for publication are copy-edited to improve readability and to ensure conformity with house style.
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