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基于韩国潜在队列的短期,中期和长期自杀企图预测模型的开发和验证

Jeong Hun Yang¹, Ri-Ra Kang², Dae Hun Kang³

¹Department of Psychiatry, Chungnam National University Sejong Hospital, 20, Bodeum 7-ro, Sejong-si 30099, Republic of Korea; Department of Psychiatry, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea.

Asian journal of psychiatry

|March 9, 2025

概括

预测高风险个体的自杀企图是可以使用传统和机器学习模型的. 虽然内部准确,但外部验证显示这些自杀预测模型的预测能力是可以接受的,尽管减少了.

科学领域:

精神病学是一个精神病学.
数据科学数据科学数据科学
公共卫生公共卫生

背景情况:

自杀仍然是一个重要的公共卫生问题,特别是在高风险人群中.
开发准确的预测模型对于及时干预和预防策略至关重要.

研究的目的:

开发和验证短期,中期和长期自杀企图的预测模型.
评估传统统计模型和机器学习算法在预测自杀企图方面的表现.

主要方法:

利用来自韩国 (2015-2023) 的K-COMPASS队列 (N=1246) 的数据.
使用后勤回归,Cox比例危险模型,随机森林,XGBoost和随机生存森林.
对所有开发的预测模型进行了内部和外部验证.

主要成果:

在内部验证中,机器学习模型的准确性高于传统模型 (AUC: 0.8454-1.0000).
这两种模型类型在外部验证中都显示了下降但可以接受的准确性 (AUC:ML的0.5948-0.7030,后勤回归的0.5958-0.7051).
发现的关键预测因素包括年轻的年龄,自杀企图的历史,抑郁和焦虑.

结论:

传统模型和机器学习模型都为自杀企图提供了可接受的预测准确性,特别是在内部验证的情况下.
外部验证结果强调了对强大,可通用模型的需求,性能下降但仍然可接受.
自我报告的数据和缺乏药物细节可能会影响这些自杀预测模型的准确性.

关键词:

考克斯的比例危险性外部验证的验证方法机器学习是机器学习.风险预测模型的风险预测模型.自杀自杀自杀自杀自杀自杀自杀自杀自杀自杀试图自杀的自杀企图.

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