Automatic prediction of mortality in patients with mental illness using
  electronic health records
Sean Kim Samuel Kim
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.