CAMP: Co-Attention Memory Networks for Diagnosis Prediction in Healthcare

Jingyue Gao, Xiting Wang, Yasha Wang, Zhao Yang, Junyi Gao, Jiangtao Wang, Wen Tang, Xing Xie

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

4 Citations (Scopus)

Abstract

Diagnosis prediction, which aims to predict future health information of patients from historical electronic health records (EHRs), is a core research task in personalized healthcare. Although some RNN-based methods have been proposed to model sequential EHR data, these methods have two major issues. First, they cannot capture fine-grained progression patterns of patient health conditions. Second, they do not consider the mutual effect between important context (e.g., patient demographics) and historical diagnosis. To tackle these challenges, we propose a model called Co-Attention Memory networks for diagnosis Prediction (CAMP), which tightly integrates historical records, fine-grained patient conditions, and demographics with a three-way interaction architecture built on co-attention. Our model augments RNNs with a memory network to enrich the representation capacity. The memory network enables analysis of fine-grained patient conditions by explicitly incorporating a taxonomy of diseases into an array of memory slots. We instantiate the READ/WRITE operations of the memory network so that the memory cooperates effectively with the patient demographics through co-attention mechanism. Experiments on real-world datasets demonstrate that CAMP consistently performs better than state-of-the-art methods.
Original languageEnglish
Title of host publicationProceedings - 19th IEEE International Conference on Data Mining, ICDM 2019
EditorsJianyong Wang, Kyuseok Shim, Xindong Wu
PublisherIEEE
Pages1036-1041
Number of pages6
ISBN (Electronic)9781728146034, 9781728146041
ISBN (Print)9781728146058
DOIs
Publication statusPublished - Nov 2019
Externally publishedYes

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2019-November
ISSN (Print)1550-4786

Keywords

  • Attention mechanism
  • Diagnosis prediction
  • Healthcare informatics
  • Memory networks

ASJC Scopus subject areas

  • Engineering(all)

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