Multimodal temporal-clinical note network for mortality prediction.

Journal: Journal of biomedical semantics
Published Date:

Abstract

BACKGROUND: Mortality prediction is an important task to achieve smart healthcare, especially for the management of intensive care unit. It can provide a reference for doctors to quickly predict the course of disease and customize early intervention programs for the patients in need. With the development of the electronic medical records, deep learning methods are introduced to deal with the prediction task. In the electronic medical records, clinical notes always contain rich and diverse medical information, including the clinical histories and reports during admission. Mortality prediction methods mostly rely on the temporal events such as medical examinations and ignore the related reports and history information in the clinical notes. We hope that we can utilize both temporal events and clinical notes information to get better mortality prediction results.

Authors

  • Haiyang Yang
    School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
  • Li Kuang
    Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • FengQiang Xia
    Changsha Hospital of Hunan Normal University, Changsha, China.