Enhancing efficiency and capacity of telehealth services with intelligent triage: a bidirectional LSTM neural network model employing character embedding.

Journal: BMC medical informatics and decision making
Published Date: Nov 21, 2023

Abstract

BACKGROUND: The widespread adoption of telehealth services necessitates accurate online department selection based on patient medical records, a task requiring significant medical knowledge. Incorrect triage results in considerable time wastage for both patients and medical professionals. To address this, we propose an intelligent triage model based on a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network with character embedding to enhance the efficiency and capacity of telehealth services.

Authors

  • Jinming Shi
    National Engineering Laboratory for Internet Medical Systems and Applications, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
  • Ming Ye
    Department of Scientific Computing, Florida State University, Tallahassee, FL 32306, USA.
  • Haotian Chen
    Department of Pharmaceutical Analysis, School of Pharmacy, China Pharmaceutical University, Nanjing 211198, China.
  • Yaoen Lu
    National Telemedicine Center of China, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
  • Zhongke Tan
    National Engineering Laboratory for Internet Medical Systems and Applications, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, Erqi District, Zhengzhou, Henan, 450052, China.
  • Zhaohan Fan
    National Engineering Laboratory for Internet Medical Systems and Applications, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, Erqi District, Zhengzhou, Henan, 450052, China.
  • Jie Zhao
    Department of Liver & Gallbladder Surgery, The First People's Hospital, Shangqiu, Henan, China.

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