Analysis on Benefits and Costs of Machine Learning-Based Early Hospitalization Prediction

Eunbi Kim, Kap Su Han, Taesu Cheong, Sung Woo Lee, Joonyup Eun, Su Jin Kim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Overcrowding in emergency departments (EDs) has long been a problem worldwide and has serious consequences for patient satisfaction and safety. Typically, overcrowding is caused by delays in the boarding time of ED patients waiting for inpatient beds. If the hospitalization of patients is predicted early enough in EDs, inpatient beds can be prepared in advance and the boarding time can be reduced. We design machine learning-based hospitalization predictive models using data on 27,747 patients and compare the experimental results. Five predictive models are designed: 1) logistic regression, 2) XGBoost, 3) NGBoost, 4) support vector machine, and 5) decision tree models. Based on the predictive results, we estimate the quantitative effects of hospitalization predictions on EDs and wards. Using the data from the ED of a general hospital in South Korea, our experiments show that the ED length of stay of a patient can be reduced by 12.3 minutes on average and the ED can reduce the total length of stay by 333,887 minutes for a year.

Original languageEnglish
Pages (from-to)32479-32493
Number of pages15
JournalIEEE Access
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Emergency department
  • estimation of quantitative effects
  • hospitalization prediction
  • machine learning

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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