Early ICU Mortality Prediction for Respiratory Failure by A Regression-Based Hazard Markov Model

Respiratory failure is one of the major causes of death in critical care units. While respiratory failure could come with the acute symptoms progressively, an early warning model is urgently required to assess mortality risks in advance. To this end, an early mortality risk prediction in patients who suffer respiratory failures can provide support for timely decision making of clinical treatment and medical resource management. In the study, we propose a dynamic modeling approach for early mortality risk prediction of at-risk patients with respiratory failure based on the first 24 hours of ICU physiological data. Our proposed model is validated on the eICU Collaborate Research Database. We achieve high AUROC performance of around 80% and significantly improved AUCPR by 4% from Day 4 to Day 6 since ICU admission, compared to the state-of-art prediction models. Furthermore, we show the survival probability curve that contains the time-varying information for early ICU admission patients.