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Using Data-Driven Machine Learning to Predict Unplanned ICU Transfers with Critical Deterioration from Electronic Health Records
Lingyun Shi, Naveen Muthu, Gerald P. Shaeffer, Yujie Sun, Victor M. Ruiz Herrera, Fuchiang R. Tsui
660 - 664
10.3233/SHTI220160
Research Article
Studies in Health Technology and Informatics
Volume 290: MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation
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.

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