Two-stage monitoring of patients in intensive care unit for sepsis prediction using non-overfitted machine learning models

Abstract

The presented research faces the problem of early detection of sepsis for patients in the Intensive Care Unit. The PhysioNet/Computing in Cardiology Challenge 2019 facilitated the development of automated, open-source algorithms for the early detection of sepsis from clinical data. A labeled clinical records dataset for training and verification of the algorithms was provided by the challenge organizers. However, a relatively small number of records with sepsis, supported by Sepsis-3 clinical criteria, led to highly unbalanced dataset (only 2% records with sepsis label). A high number of unbalanced data records is a great challenge for machine learning model training and is not suitable for training classical classifiers. To address these issues, a method taking into the account the amount of time the patients spent in the intensive care unit (ICU) was proposed. The proposed method uses two separate ensemble models, one trained on patient records under 56 h in the ICU, and another for patients who stayed longer than 56 h. A solution including feature selection and weighting based training on imbalanced data was proposed in this paper. In addition, several performance metrics were investigated. Results show, that for successful prediction, a particular model having few or more predictors based on the length of stay in the Intensive Care Unit should be applied.