Lin I-Li, Tseng Jean Yu-Chen, Tung Hui-Ting, Hu Ya-Han, You Zi-Hung
Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 600, Taiwan.
Department of Public Affairs, Fo-Guang University, Yilan 262, Taiwan.
Healthcare (Basel). 2022 Apr 2;10(4):667. doi: 10.3390/healthcare10040667.
Suicide is listed in the top ten causes of death in Taiwan. Previous studies have pointed out that psychiatric patients having suicide attempts in their history are more likely to attempt suicide again than non-psychiatric patients. Therefore, how to predict the future multiple suicide attempts of psychiatric patients is an important issue of public health. Different from previous studies, we collect the psychiatric patients who have a suicide diagnosis in the National Health Insurance Research Database (NHIRD) as the study cohort. Study variables include psychiatric patients' characteristics, medical behavior characteristics, physician characteristics, and hospital characteristics. Three machine learning techniques, including decision tree (DT), support vector machine (SVM), and artificial neural network (ANN), are used to develop models for predicting the risk of future multiple suicide attempts. The Adaboost technique is further used to improve prediction performance in model development. The experimental results show that Adaboost+DT performs the best in predicting the behavior of multiple suicide attempts among psychiatric patients. The findings of this study can help clinical staffs to early identify high-risk patients and improve the effectiveness of suicide prevention.
自杀位列台湾十大死因之中。先前的研究指出，有自杀未遂史的精神科患者比非精神科患者更有可能再次尝试自杀。因此，如何预测精神科患者未来的多次自杀企图是一个重要的公共卫生问题。与先前的研究不同，我们在国民健康保险研究数据库（NHIRD）中收集有自杀诊断记录的精神科患者作为研究队列。研究变量包括精神科患者的特征、医疗行为特征、医生特征和医院特征。三种机器学习技术，包括决策树（DT）、支持向量机（SVM）和人工神经网络（ANN），被用于开发预测未来多次自杀企图风险的模型。在模型开发中进一步使用Adaboost技术来提高预测性能。实验结果表明，Adaboost+DT在预测精神科患者多次自杀企图行为方面表现最佳。本研究结果有助于临床工作人员早期识别高危患者并提高自杀预防的有效性。
