An ensemble model for predicting dispositions of emergency department patients.

Journal: BMC medical informatics and decision making
Published Date: Apr 22, 2024

Abstract

OBJECTIVE: The healthcare challenge driven by an aging population and rising demand is one of the most pressing issues leading to emergency department (ED) overcrowding. An emerging solution lies in machine learning's potential to predict ED dispositions, thus leading to promising substantial benefits. This study's objective is to create a predictive model for ED patient dispositions by employing ensemble learning. It harnesses diverse data types, including structured and unstructured information gathered during ED visits to address the evolving needs of localized healthcare systems.

Authors

  • Kuang-Ming Kuo
    Department of Healthcare Administration, I-Shou University, Kaohsiung City, Taiwan, ROC.
  • Yih-Lon Lin
    Department of Information Engineering, I-Shou University, Kaohsiung, Taiwan.
  • Chao Sheng Chang
    Department of Emergency Medicine, E-Da Hospital, Kaohsiung City, Taiwan. zincfinger522@yahoo.com.tw.
  • Tin Ju Kuo
    Department of Computer Science and Information Engineering, National Taitung University, 369, Sec. 2, University Rd, Taitung, Taiwan.

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