Early short-term prediction of emergency department length of stay using natural language processing for low-acuity outpatients.

Journal: The American journal of emergency medicine
PMID: 32216994

Abstract

BACKGROUND: Low-acuity outpatients constitute the majority of emergency department (ED) patients, and these patients often experience an unpredictable length of stay (LOS). Effective LOS prediction might improve the quality of ED care and reduce ED crowding.

Authors

  • Chien-Hua Chen
    Department of Electrical Engineering, I-Shou University, Kaohsiung, Taiwan; Department of Emergency Medicine, Taichung Veterans General Hospital Chiayi Branch, Chia-Yi, Taiwan.
  • Jer-Guang Hsieh
    Department of Electrical Engineering, I-Shou University, Kaohsiung, Taiwan.
  • Shu-Ling Cheng
    Department of Multimedia and Game Developing Management, Far East University, Tainan, Taiwan. Electronic address: emily.shuling@gmail.com.
  • Yih-Lon Lin
    Department of Information Engineering, I-Shou University, Kaohsiung, Taiwan.
  • Po-Hsiang Lin
    Department of Electrical Engineering, I-Shou University, Kaohsiung, Taiwan; Department of Emergency Medicine, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.
  • Jyh-Horng Jeng
    Department of Information Engineering, I-Shou University, Kaohsiung, Taiwan.

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