Suicide is the tenth leading cause of death in the United States (US). An early-warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts, and analyzing the contribution of repeated attempts to the risk of eventual death by suicide. In this study we sought to develop an EWS for high-risk suicide attempt patients through the development of a population-based risk stratification surveillance system. Advanced machine-learning algorithms and deep neural networks were utilized to build models with the data from electronic health records (EHRs). A final risk score was calculated for each individual and calibrated to indicate the probability of a suicide attempt in the following 1-year time period. Risk scores were subjected to individual-level analysis in order to aid in the interpretation of the results for health-care providers managing the at-risk cohorts. The 1-year suicide attempt risk model attained an area under the curve (AUC ROC) of 0.792 and 0.769 in the retrospective and prospective cohorts, respectively. The suicide attempt rate in the "very high risk" category was 60 times greater than the population baseline when tested in the prospective cohorts. Mental health disorders including depression, bipolar disorders and anxiety, along with substance abuse, impulse control disorders, clinical utilization indicators, and socioeconomic determinants were recognized as significant features associated with incident suicide attempt.

中文翻译：

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使用深度学习和电子健康记录为自杀企图高危患者开发预警系统。

自杀是美国第十大死因。自杀未遂预警系统（EWS）对于识别那些有自杀未遂风险的人以及分析反复尝试对最终自杀死亡风险的影响可能很有价值。在这项研究中，我们试图通过开发基于人群的风险分层监测系统，为高风险自杀未遂患者开发 EWS。利用先进的机器学习算法和深度神经网络，利用电子健康记录 (EHR) 中的数据构建模型。计算每个人的最终风险评分并进行校准，以表明在接下来的一年时间内自杀未遂的概率。对风险评分进行了个体层面的分析，以帮助管理高危人群的医疗保健提供者解释结果。1 年自杀未遂风险模型在回顾性和前瞻性队列中的曲线下面积 (AUC ROC) 分别为 0.792 和 0.769。在前瞻性队列中进行测试时，“极高风险”类别中的自杀未遂率比人群基线高 60 倍。精神健康障碍，包括抑郁症、躁郁症和焦虑症，以及药物滥用、冲动控制障碍、临床利用指标和社会经济决定因素，被认为是与自杀未遂事件相关的重要特征。