Development, Validation, and Deployment of an Interpretable Machine Learning Model for In-Hospital Mortality Prediction in Critically Ill Patients with COPD and Sepsis: A Multicenter Cohort Study

Background

Critically ill patients with chronic obstructive pulmonary disease (COPD) and sepsis are at high risk of in-hospital mortality because of respiratory and organ dysfunction. Early risk assessment may be useful for clinical decision-making. This study aimed to develop an interpretable machine learning model for this population.