Mental disorders impact the lives of millions of people globally, not only
impeding their day-to-day lives but also markedly reducing life expectancy.
This paper addresses the persistent challenge of predicting mortality in
patients with mental diagnoses using predictive machine-learning models with
electronic health records (EHR). Data from patients with mental disease
diagnoses were extracted from the well-known clinical MIMIC-III data set
utilizing demographic, prescription, and procedural information. Four machine
learning algorithms (Logistic Regression, Random Forest, Support Vector
Machine, and K-Nearest Neighbors) were used, with results indicating that
Random Forest and Support Vector Machine models outperformed others, with AUC
scores of 0.911. Feature importance analysis revealed that drug prescriptions,
particularly Morphine Sulfate, play a pivotal role in prediction. We applied a
variety of machine learning algorithms to predict 30-day mortality followed by
feature importance analysis. This study can be used to assist hospital workers
in identifying at-risk patients to reduce excess mortality.

使用预测性机器学习模型和电子健康记录来预测心理诊断患者的死亡率所带来的持久挑战，机器学习算法（逻辑回归、随机森林、支持向量机和 k 近邻）的结果表明随机森林和支持向量机模型优于其他模型，AUC 得分为 0.911，药物处方，尤其是硫酸吗啡在预测中起到关键作用。这项研究可用于帮助医院工作人员识别有风险的患者，减少过度死亡。