Comparison of machine learning algorithms for mortality prediction in intensive care patients on multi-center critical care databases

Abstract

Current scoring systems for mortality prediction in intensive care patients are usually applied once after 24 hours of admission, as all parameters needed for scoring are not yet available. In addition, several parameters are dynamic and may change according to patient conditions. It is hypothesized that mortality prediction should be made at the earliest when relevant information becomes available and continuously during patient stay. This study focuses on the development of algorithms for mortality prediction from vital signs and laboratory results based on the data from three recent critical care databases, i.e. the eICU collaborative research database, the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III) database, and the MIMIC-IV database. We employed logistic regression, κ-nearest neighbours, neural networks and tree-based classifiers for such problem. Our models had the area under the receiver operating characteristic curve (AUROC) ranging from 0.67 – 0.95. Reliable mortality prediction can be made as early as the first 4 hours after ICU admission. We provided comprehensive analysis on different time frames used for prediction, models trained with top attributes, models trained with data combination, and missing values. Our results provide guidelines and benchmarks for the development of such algorithm in local narratives.