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Using Data-Driven Machine Learning to Predict Unplanned ICU Transfers with Critical Deterioration from Electronic Health Records.
- Source :
- Medinfo; 2021, Vol. 290, p660-664, 5p
- Publication Year :
- 2021
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- INTENSIVE care units
PREDICTIVE tests
CONFIDENCE intervals
CHILDREN'S hospitals
MACHINE learning
CONFERENCES & conventions
RETROSPECTIVE studies
REGRESSION analysis
HOSPITAL care
HOSPITAL wards
RESEARCH funding
ELECTRONIC health records
SENSITIVITY & specificity (Statistics)
ARTIFICIAL neural networks
RECEIVER operating characteristic curves
HOSPITAL care of children
LONGITUDINAL method
NURSING assessment
Subjects
Details
- Language :
- English
- ISSN :
- 15696332
- Volume :
- 290
- Database :
- Complementary Index
- Journal :
- Medinfo
- Publication Type :
- Conference
- Accession number :
- 157834239
- Full Text :
- https://doi.org/10.3233/SHTI220160