Interpretable time-aware and co-occurrence-aware network for medical prediction.

Journal: BMC medical informatics and decision making
Published Date:

Abstract

BACKGROUND: Disease prediction based on electronic health records (EHRs) is essential for personalized healthcare. But it's hard due to the special data structure and the interpretability requirement of methods. The structure of EHR is hierarchical: each patient has a sequence of admissions, and each admission has some co-occurrence diagnoses. However, the existing methods only partially model these characteristics and lack the interpretation for non-specialists.

Authors

  • Chenxi Sun
    School of Electronics Engineering and Computer Science, Peking University, Beijing, People's Republic of China.
  • Hongna Dui
    The Aviation Industry Corporation of China, Ltd, Chengdu Aircraft Design & Research Institute, Chengdu, 610041, People's Republic of China.
  • Hongyan Li
    Department of Psychogeriatrics, Kangci Hospital of Jiaxing, Tongxiang, Zhejiang, China.