Medication Combination Prediction Using Temporal Attention Mechanism and Simple Graph Convolution

Haiqiang Wang, Yinying Wu, Chao Gao*, Yue Deng, Fan Zhang, Jiajin Huang, Jiming Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Medication combination prediction can be applied to the clinical treatment for critical patients with multi-morbidity. The suitable medication combination can help cure patients and keep the treatment medication safe. However, the complexity and uncertainty of clinical circumstances limit the predictive accuracy of medication combination. Thus, this paper proposes a new medication combination prediction model based on the temporal attention mechanism (TAM) and the simple graph convolution (SGC), named as TAMSGC. More specifically, the TAM can capture the temporal sequence information in the medical records, and the SGC is implemented to acquire the medication knowledge from the complicated medication combination. Experiments in a real dataset show that TAMSGC surpasses the baseline models on the predictive accuracy of medication combination.

Original languageEnglish
Pages (from-to)3995-4004
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Volume25
Issue number10
Early online date21 May 2021
DOIs
Publication statusPublished - Oct 2021

Scopus Subject Areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

User-Defined Keywords

  • attention mechanism
  • critical patients
  • medical records
  • Medication combination prediction
  • medication knowledge
  • simple graph convolution
  • temporal sequence

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