Disease Diagnosis Prediction of EMR Based on BiGRU-Att-CapsNetwork Model

Pin Ni, Yuming Li, Jiayi Zhu, Junkun Peng, Zhenjin Dai, Gangmin Li, Xuming Bai

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

11 Citations (Scopus)

Abstract

Electronic Medical Records (EMR) carry a large number of diseases characteristics, history and other specific details of patients, which has great value for medical diagnosis. These data with diagnostic labels can help automated diagnostic assistant to predict disease diagnosis and provide a rapid diagnostic reference for doctors. In this study, we designed a BiGRU-Att-CapsNetwork model based on our proposed CMedBERT Chinese medical domain pre-trained language model to predict disease diagnosis in Chinese EMR. In the wide-ranging comparative experiments involving a real EMR dataset (SAHSU) and an academic evaluation task dataset (CCKS 2019), our model obtained competitive performance.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
EditorsChaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6166-6168
Number of pages3
ISBN (Electronic)9781728108582
DOIs
Publication statusPublished - Dec 2019
Event2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States
Duration: 9 Dec 201912 Dec 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference2019 IEEE International Conference on Big Data, Big Data 2019
Country/TerritoryUnited States
CityLos Angeles
Period9/12/1912/12/19

Keywords

  • Attention
  • BiGRU
  • Capsule Network
  • Disease Diagnosis Prediction
  • EMR

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