Emergency department disposition prediction using a deep neural network with integrated clinical narratives and structured data.

Journal: International journal of medical informatics
Published Date: Apr 23, 2020

Abstract

BACKGROUND: Emergency department (ED) overcrowding has been a serious issue and demands effective clinical decision-making of patient disposition. In previous studies, emergency clinical narratives provide a rich context for clinical decisions. We aimed to develop the disposition prediction model using deep learning modeling strategy with the heterogeneous data, including the physicians' narratives.

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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