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Predicting hospital admission at emergency department triage using machine learning

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  • Woo Suk Hong
  • Adrian Daniel Haimovich
  • R Andrew Taylor

Abstract

Objective: To predict hospital admission at the time of ED triage using patient history in addition to information collected at triage. Methods: This retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model. Results: A total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86–0.87), 0.87 for XGBoost (95% CI 0.87–0.88) and 0.87 for DNN (95% CI 0.87–0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86–0.87), 0.87 for XGBoost (95% CI 0.87–0.87) and 0.87 for DNN (95% CI 0.87–0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91–0.91), 0.92 for XGBoost (95% CI 0.92–0.93) and 0.92 for DNN (95% CI 0.92–0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91–0.91). Conclusion: Machine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.

Suggested Citation

  • Woo Suk Hong & Adrian Daniel Haimovich & R Andrew Taylor, 2018. "Predicting hospital admission at emergency department triage using machine learning," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-13, July.
  • Handle: RePEc:plo:pone00:0201016
    DOI: 10.1371/journal.pone.0201016
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    References listed on IDEAS

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    1. Stephen F Weng & Jenna Reps & Joe Kai & Jonathan M Garibaldi & Nadeem Qureshi, 2017. "Can machine-learning improve cardiovascular risk prediction using routine clinical data?," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-14, April.
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    1. Hyeram Seo & Imjin Ahn & Hansle Gwon & Hee Jun Kang & Yunha Kim & Ha Na Cho & Heejung Choi & Minkyoung Kim & Jiye Han & Gaeun Kee & Seohyun Park & Dong-Woo Seo & Tae Joon Jun & Young-Hak Kim, 2024. "Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data," Health Care Management Science, Springer, vol. 27(1), pages 114-129, March.
    2. Douglas Spangler & Thomas Hermansson & David Smekal & Hans Blomberg, 2019. "A validation of machine learning-based risk scores in the prehospital setting," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-18, December.
    3. Emilien Arnaud & Mahmoud Elbattah & Christine Ammirati & Gilles Dequen & Daniel Aiham Ghazali, 2022. "Use of Artificial Intelligence to Manage Patient Flow in Emergency Department during the COVID-19 Pandemic: A Prospective, Single-Center Study," IJERPH, MDPI, vol. 19(15), pages 1-13, August.
    4. Julian Schiele & Thomas Koperna & Jens O. Brunner, 2021. "Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(1), pages 65-88, February.

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