Incorporating Laboratory Values Into a Machine Learning Model Improves In-Hospital Mortality Predictions After Rapid Response Team Call

Abstract

OBJECTIVES: Machine learning models have been used to predict mortality among patients requiring rapid response team activation. The goal of our study was to assess the impact of adding laboratory values into the model. DESIGN: A gradient boosted decision tree model was derived and internally validated to predict a primary outcome of in-hospital mortality. The base model was then augmented with laboratory values. SETTING: Two tertiary care hospitals within The Ottawa Hospital network. PATIENTS: Inpatients over the age of 18 years who experienced a rapid response team activation between January 1, 2015, and May 31, 2016. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: ), length of stay prior to rapid response team activation, and systolic blood pressure. CONCLUSIONS: Machine learning models can identify rapid response team patients at a high risk of mortality and potentially supplement clinical decision making. Incorporating laboratory values into model development significantly improved predictive performance in this study.

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