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http://hdl.handle.net/11054/3200| Title: | Predicting the emergency department patient journey using a machine learning approach. |
| Author: | Kovoor, Joshua Carmichael, Gavin J. Stretton, B. Gupta, A. K. Kleinig, O. S. Ittimani, M. Fabian, J. Tan, S. Ng, J. S. Sateakeerthy, S. Booth, A. Beath, Alexander Kefalianos, John Jacob, Mathew O. Ahmed, S. Chan, W. Kovoor, P. Gluck, S. Gilbert, T. Malycha, J. Reddi, B. A. Padbury, R. T. Trochsler, M. I. Maddern, G. J. Chew, D. P. Zannettino, A. C. Liew, D. Beltrame, J. F. O’Callaghan, P. G. Papendick, C. Bacchi, S. |
| Issue Date: | 2025 |
| Publication Title: | JMIR AI |
| Volume: | 4 |
| Abstract: | We aimed to evaluate the performance of various ML models in predicting three key outcomes in ED patients’ journeys: prolonged ED length of stay (LOS ≥8 h), chest x-ray (CXR) utilization, and inpatient admissions. We analyzed data from 50,000 ED visits at two major public metropolitan hospitals in South Australia and tested XGBoost (extreme gradient boosting), random forest, and logistic regression models. Our primary objective was to assess model accuracy in predicting the outcomes to support clinical decision-making and enhance operational efficiency. |
| URI: | http://hdl.handle.net/11054/3200 |
| DOI: | https://ai.jmir.org/2025/1/e67321 |
| Internal ID Number: | 03139 |
| Health Subject: | ARTIFICIAL INTELLIGENCE PREDICTION MACHINE LEARNING HOSPITAL RESOURCE MANAGEMENT |
| Type: | Journal Article Article |
| Appears in Collections: | Research Output |
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