MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model.

ABSTRACT: Early detection of patients at risk for clinical deterioration is crucial for timely intervention. Traditional detection systems rely on a limited set of variables and are unable to predict the time of decline. We describe a machine learning model called MEWS++ that enables the identification of patients at risk of escalation of care or death six hours prior to the event. A retrospective single-center cohort study was conducted from July 2011 to July 2017 of adult (age > 18) inpatients excluding psychiatric, parturient, and hospice patients. Three machine learning models were trained and tested: random forest (RF), linear support vector machine, and logistic regression. We compared the models' performance to the traditional Modified Early Warning Score (MEWS) using sensitivity, specificity

SUBMITTER: Kia A 

PROVIDER: S-EPMC7073544 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature