Though suicide is a major public health problem in the US, machine learning methods are not commonly used to predict an individual's risk of attempting/committing suicide. In the present work, starting with an anonymized collection of electronic health records for 522,056 unique, California-resident adolescents, we develop neural network models to predict suicide attempts. We frame the problem as a binary classification problem in which we use a patient's data from 2006-2009 to predict either the presence (1) or absence (0) of a suicide attempt in 2010. After addressing issues such as severely imbalanced classes and the variable length of a patient's history, we build neural networks with depths varying from one to eight hidden layers. For test set observations with at least one prior diagnosis in the category of schizophrenia and psychotic disorders, our model achieves a sensitivity of 0.78, specificity of 0.91, AUC of 0.92, and overall accuracy of 88%. We detail these and similar results, and then pose open questions for future work.

本文利用深度神经网络模型，通过机器学习方法处理现有的电子健康记录，在522,056个加州青少年的历史记录数据中，预测出其在2010年是否有自杀未遂行为（二元分类问题），并取得了敏感度为0.703，特异度为0.980和AUC为0.958的良好预测效果。

使用神经网络预测青少年自杀尝试