Critical care staff are presented with a large amount of data, which made it difficult to systematically evaluate. Early detection of patients whose condition is deteriorating could reduce mortality, improve treatment outcomes, and allow a better use of healthcare resources. In this study, we propose a data-driven framework for predicting the risk of mortality that combines high-accuracy recurrent neural networks with interpretable explanations. Our model processes time-series of vital signs and laboratory observations to predict the probability of a patient’s mortality in the intensive care unit (ICU). We investigated our approach on three public critical care databases: Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III), MIMIC-IV, and eICU. Our models achieved an area under the receiver operating characteristic curve (AUC) of 0.87–0.91. Our approach was not only able to provide the predicted mortality risk but also to recognize and explain the historical contributions of the associated factors to the prediction. The explanations provided by our model were consistent with the literature. Patients may benefit from early intervention if their clinical observations in the ICU are continuously monitored in real time.

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.

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