A Novel Switching State-Space Model for Post-ICU Mortality Prediction and Survival Analysis

Predicting mortality risk in patients accurately during and after intensive care unit (ICU) stay is an essential component for supporting critical care decision-making. To date, various scoring systems have been designed for survival analysis and mortality prediction by providing risk scores based on patient's vital signs and lab results. However, it is challenging using these universal scores to represent the overall severity level of illness and to look into patient's deterioration leading to high mortality risk during ICU stay. Thus, a close monitoring of the severity level over time during ICU stay is more preferable. In this study, we design a new switching state-space model by correlating patient's condition dynamics in last hours of ICU stay to the risk probabilities in a short time period (1–6 days) after ICU discharge. More specifically, we propose to integrate a cumulative hazard function estimating survival probability into the autoregressive hidden Markov model using time-interval sequential SAPS II scores as features. We demonstrate the significant improvement of mortality prediction comparing to SAPS I, SAPS II, and SOFA scoring systems for the PhysioNet MIMIC II Challenge data.