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Using machine‐learning methods to support health‐care professionals in making admission decisions

Author

Listed:
  • Li Luo
  • Jialing Li
  • Chuang Liu
  • Wenwu Shen

Abstract

Background Large tertiary hospitals usually face long waiting lines; patients who want to receive hospitalization need to be screened in advance. The patient admission screening process involves a health‐care professional ranking patients by analyzing registration information. Objective The purpose of this study was to develop a machine‐learning approach to screening, using historical data and the experience of health‐care professionals to develop a set of screening rules to help health‐care professionals prioritize patient needs automatically. Methods We used five machine‐learning methods to sequence and predict elective patients: logistic regression (LR), random forest (RF), gradient‐boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and an ensemble model of the four models. Results The results indicate that all of the five models showed a good prioritization performance with high predictive values. In particular, XGBoost had the best predictive performance compared with others in terms of the area under the receiver operating characteristic curve (AUC), with the AUC values of LR, RF, GBDT, XGBoost, and the ensemble model being 0.881, 0.816, 0.820, 0.901, and 0.897, respectively. Conclusion The results reported here indicate that machine‐learning techniques can be valuable for automating the screening process. Our model can assist health‐care professionals in automatically evaluating less complex cases by identifying important factors affecting patient admission.

Suggested Citation

  • Li Luo & Jialing Li & Chuang Liu & Wenwu Shen, 2019. "Using machine‐learning methods to support health‐care professionals in making admission decisions," International Journal of Health Planning and Management, Wiley Blackwell, vol. 34(2), pages 1236-1246, April.
  • Handle: RePEc:bla:ijhplm:v:34:y:2019:i:2:p:e1236-e1246
    DOI: 10.1002/hpm.2769
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    1. Jens Kjølseth Møller & Martin Sørensen & Christian Hardahl, 2021. "Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: A retrospective cohort study," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-16, March.
    2. Isabel Marques & Zélia Serrasqueiro & Fernanda Nogueira, 2021. "Managers’ Competences in Private Hospitals for Investment Decisions during the COVID-19 Pandemic," Sustainability, MDPI, vol. 13(4), pages 1-14, February.
    3. Esra Zihni & Vince Istvan Madai & Michelle Livne & Ivana Galinovic & Ahmed A Khalil & Jochen B Fiebach & Dietmar Frey, 2020. "Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-15, April.

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