Application of the Bidirectional Encoder Representations from Transformers Model for Predicting the Abbreviated Injury Scale in Patients with Trauma: Algorithm Development and Validation Study.

Journal: JMIR formative research
Published Date:

Abstract

BACKGROUND: Deaths related to physical trauma impose a heavy burden on society, and the Abbreviated Injury Scale (AIS) is an important tool for injury research. AIS covers injuries to various parts of the human body and scores them based on the severity of the injury. In practical applications, the complex AIS coding rules require experts to encode by consulting patient medical records, which inevitably increases the difficulty, time, and cost of evaluation of patient and also puts higher demands on the workload of information collection and processing. In some cases, the sheer number of patients or the inability to access detailed medical records necessary for coding further complicates independent AIS codes.

Authors

  • Jun Tang
    School of Electronics and Information Engineering, Anhui University, Hefei, China.
  • Yang Li
    Occupation of Chinese Center for Disease Control and Prevention, Beijing, China.
  • Keyu Luo
    Department of Orthopedics, Daping Hospital, Army Medical University, No.10 Daping Changjiang Branch Road, Yuzhong District, Chongqing City, Chongqing, 400042, China.
  • Jiangyuan Lai
    Department of Traumatic Surgery, School of Basic Medicine, Army Medical University, Chongqing, 400042, China.
  • Xiang Yin
    College of Environmental Science and Engineering, Biomedical Multidisciplinary Innovation Research Institute, Shanghai East Hospital, Tongji University, Shanghai 200092, People's Republic of China.
  • Dongdong Wu
    Department of Information, Research Institute of Field Surgery, Daping Hospital of Army Medical University, 10 Changjiang Access Road, Chongqing, 400042, China.