OBJECTIVE We aimed to develop a data-driven machine learning model for predicting critical deterioration events from routinely collected EHR data in hospitalized children. MATERIALS This retrospective cohort study included all… Click to show full abstract
OBJECTIVE We aimed to develop a data-driven machine learning model for predicting critical deterioration events from routinely collected EHR data in hospitalized children. MATERIALS This retrospective cohort study included all pediatric inpatients hospitalized on a medical or surgical ward between 2014-2018 at a quaternary children's hospital. METHODS We developed a large data-driven approach and evaluated three machine learning models to predict pediatric critical deterioration events. We evaluated the models using a nested, stratified 10-fold cross-validation. The evaluation metrics included C-statistic, sensitivity, and positive predictive value. We also compared the machine learning models with patients identified as high-risk Watchers by bedside clinicians. RESULTS The study included 57,233 inpatient admissions from 34,976 unique patients. 3,943 variables were identified from the EHR data. The XGBoost model performed best (C-statistic=0.951, CI: 0.946 ∼ 0.956). CONCLUSIONS Our data-driven machine learning models accurately predicted patient deterioration. Future sociotechnical analysis will inform deployment within the clinical setting.
               
Click one of the above tabs to view related content.