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.

翻译：精神障碍影响着全球数百万人的生活，不仅妨碍其日常生活，还显著缩短预期寿命。本文探讨了利用电子健康记录（EHR）中的预测性机器学习模型预测精神疾病患者死亡率的持续挑战。我们从著名的临床MIMIC-III数据集中提取了具有精神疾病诊断的患者数据，其中包含人口统计、处方和程序信息。采用了四种机器学习算法（逻辑回归、随机森林、支持向量机和K近邻），结果表明随机森林和支持向量机模型的表现优于其他模型，AUC得分达到0.911。特征重要性分析显示，药物处方（尤其是硫酸吗啡）在预测中起着关键作用。我们应用了多种机器学习算法来预测30天死亡率，随后进行了特征重要性分析。本研究可协助医院工作人员识别高风险患者，以减少超额死亡率。