We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Machine learning for real-time aggregated prediction of hospital admission for emergency patients.
- Authors
King, Zella; Farrington, Joseph; Utley, Martin; Kung, Enoch; Elkhodair, Samer; Harris, Steve; Sekula, Richard; Gillham, Jonathan; Li, Kezhi; Crowe, Sonya
- Abstract
Machine learning for hospital operations is under-studied. We present a prediction pipeline that uses live electronic health-records for patients in a UK teaching hospital's emergency department (ED) to generate short-term, probabilistic forecasts of emergency admissions. A set of XGBoost classifiers applied to 109,465 ED visits yielded AUROCs from 0.82 to 0.90 depending on elapsed visit-time at the point of prediction. Patient-level probabilities of admission were aggregated to forecast the number of admissions among current ED patients and, incorporating patients yet to arrive, total emergency admissions within specified time-windows. The pipeline gave a mean absolute error (MAE) of 4.0 admissions (mean percentage error of 17%) versus 6.5 (32%) for a benchmark metric. Models developed with 104,504 later visits during the Covid-19 pandemic gave AUROCs of 0.68–0.90 and MAE of 4.2 (30%) versus a 4.9 (33%) benchmark. We discuss how we surmounted challenges of designing and implementing models for real-time use, including temporal framing, data preparation, and changing operational conditions.
- Subjects
UNITED Kingdom; ACADEMIC medical centers; MACHINE learning; PATIENTS; HOSPITAL admission &; discharge; EMERGENCY medical services; DESCRIPTIVE statistics; SURVIVAL analysis (Biometry); ELECTRONIC health records; PREDICTION models
- Publication
NPJ Digital Medicine, 2022, Vol 5, Issue 1, p1
- ISSN
2398-6352
- Publication type
Article
- DOI
10.1038/s41746-022-00649-y