Background

Suicide is a serious social and public health problem. Social stigma and prejudice reduce the accessibility of mental health care services for high-risk groups, resulting in them not receiving interventions and committing suicide. A suicide prediction model is necessary to identify high-risk groups in the general population.

Methods

We used national medical check-up data from 2009 to 2015 in Korea. The latest medical check-up data for each subject was set as an index point. Analysis was undertaken for an overall follow-up period (index point to the final tracking period) as well as for a one-year follow-up period. The training set was cross-validated fivefold. The predictive model was trained using a random forest algorithm, and its performance was measured using a separate test set not included in the training.

Results

The analysis covered 372,813 individuals, with an average (SD) overall follow-up duration of 1.52 (1.52) years. When we predicted suicide during the overall follow-up period, the area under the receiver operating characteristic curve (AUC) was 0.849, sensitivity was 0.817, and specificity was 0.754. The performance of the predicted suicide risk model for one year from the index point was AUC 0.818, sensitivity 0.788, and specificity 0.657.

Conclusions

This is probably the first suicide predictive model using machine learning based on medical check-up data from the general population. It could be used to screen high-risk suicidal groups from the population through routine medical check-ups. Future studies may test preventive interventions such as exercise and alcohol in these high-risk groups.

中文翻译：

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在372,813个人进行身体检查时的自杀预测。

背景

自杀是一个严重的社会和公共卫生问题。社会上的污名和偏见减少了高危人群获得精神保健服务的机会，导致他们没有受到干预并自杀。为了确定普通人群中的高危人群，自杀预测模型是必要的。

方法

我们使用了2009年至2015年韩国的国家体检数据。将每个受试者的最新体检数据设置为指标。对整个随访期（最后追踪期的指标点）以及一年的随访期进行了分析。训练集被交叉验证了五倍。使用随机森林算法对预测模型进行了训练，并使用训练中未包含的单独测试集来评估其性能。

结果

该分析涵盖372,813名个体，平均（SD）总体随访时间为1.52（1.52）年。当我们预测在整个随访期间发生自杀时，受试者工作特征曲线（AUC）下的面积为0.849，灵敏度为0.817，特异性为0.754。从指标点开始，预测的自杀风险模型在一年内的表现为AUC 0.818，敏感性0.788和特异性0.657。

结论

这可能是第一个使用基于一般人群的医学检查数据的机器学习进行自杀预测的模型。它可用于通过常规体检从人群中筛查高危自杀群体。未来的研究可能会测试这些高风险人群的预防性干预措施，例如运动和酗酒。