DeepMPM: a mortality risk prediction model using longitudinal EHR data.

Journal: BMC bioinformatics
Published Date: Oct 14, 2022

Abstract

BACKGROUND: Accurate precision approaches have far not been developed for modeling mortality risk in intensive care unit (ICU) patients. Conventional mortality risk prediction methods can hardly extract the information in longitudinal electronic medical records (EHRs) effectively, since they simply aggregate the heterogeneous variables in EHRs, ignoring the complex relationship and interactions between variables and the time dependence in longitudinal records. Recently deep learning approaches have been widely used in modeling longitudinal EHR data. However, most existing deep learning-based risk prediction approaches only use the information of a single disease, neglecting the interactions between multiple diseases and different conditions.

Authors

  • Fan Yang
    School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, China.
  • Jian Zhang
    College of Pharmacy, Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China.
  • Wanyi Chen
    Department of Automation, Xiamen University, Xiamen, China.
  • Yongxuan Lai
    School of informatics/Shenzhen Research Institute, Xiamen University, Xiamen/Shenzhen, China. laiyx@xmu.edu.cn.
  • Ying Wang
    Key Laboratory of Macromolecular Science of Shaanxi Province, School of Chemistry & Chemical Engineering, Shaanxi Normal University, Xi'an, Shaanxi 710062, China.
  • Quan Zou

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