An Improved Patient-Specific Mortality Risk Prediction in ICU in a Random Forest Classification Framework

Dynamic and automatic patient specific prediction of the risk associated with ICU mortality may facilitate timely and appropriate intervention of health professionals in hospitals. In this work, patient information and time series measurements of vital signs and laboratory results from the first 48 hours of ICU stays of 4000 adult patients from a publicly available dataset are used to design and validate a mortality prediction system. An ensemble of decision trees are used to simultaneously predict and associate a risk score against each patient in a k-fold validation framework. Risk assessment prediction accuracy of 87% is achieved with our model and the results show significant improvement over a baseline algorithm of SAPS-I that is commonly used for mortality prediction in ICU. The performance of our model is further compared to other state-of-the-art algorithms evaluated on the same dataset.