A Time-Incorporated SOFA-Based Explainable Machine Learning Model for Mortality Prediction in Critically Ill Patients

Abstract Background: Organ dysfunction (OD) assessment is essential in intensive care units (ICUs). However, no OD scoring system has so far considered the duration of OD, which is clinically relevant. This study aimed to develop and validate an ICU mortality prediction model based on the Sequential Organ Failure Assessment (SOFA) score, incorporating the time dimension with machine learning methods. Methods: Data from the eICU Collaborative Research Database and Medical Information Mart for Intensive Care (MIMIC) -III were mixed for model development, and the MIMIC-IV dataset and Nanjing Jinling Hospital Surgery ICU (SICU-JL) dataset were used for external testing. Adult patients in the ICUs for more than 72 hours were deemed eligible. The total SOFA score and individual scores were calculated…