A-GSTCN: An Augmented Graph Structural-Temporal Convolution Network for Medication Recommendation Based on Electronic Health Records.

Weiqi Yue, Maiqiu Wang, Lei Zhang, Lijuan Zhang, Jie Huang, Jian Wan, Naixue Xiong, Athanasios V Vasilakos

Abstract read

In one paragraph

Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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8 authors.

Weiqi YueSchool of Electronic and Information Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.ORCID 0009-0007-8623-579X

Maiqiu WangInstitute of Biochemistry, Zhejiang University of Science and Technology, Hangzhou 310023, China.ORCID 0009-0000-8990-3510

Lei ZhangSchool of Electronic and Information Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.

Lijuan ZhangSchool of Electronic and Information Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.ORCID 0000-0002-5192-7418

Jie HuangSchool of Electronic and Information Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.

Jian WanSchool of Electronic and Information Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.

Naixue XiongDepartment of Computer Science and Mathematics, Sul Ross State University, Alpine, TX 79830, USA.ORCID 0000-0002-0394-4635

Athanasios V VasilakosThe Center for AI Research (CAIR), University of Agder (UiA), 4879 Grimstad, Norway.

Funding

Zhejiang Province Key Research and Development Project 2020C03071

6 · The paper itself

Abstract

Medication recommendation based on electronic health records (EHRs) is a significant research direction in the biomedical field, which aims to provide a reasonable prescription for patients according to their historical and current health conditions. However, the existing recommended methods have many limitations in dealing with the structural and temporal characteristics of EHRs. These methods either only consider the current state while ignoring the historical situation, or fail to adequately assess the structural correlations among various medical events. These factors result in poor recommendation quality. To solve this problem, we propose an augmented graph structural-temporal convolutional network (A-GSTCN). Firstly, an augmented graph attention network is used to model the structural features among medical events of patients' EHRs. Next, the dilated convolution combined with residual connection is applied in the proposed model, which can improve the temporal prediction capability and further reduce the complexity. Moreover, the cache memory module further enhances the model's learning of the history of EHRs. Finally, the A-GSTCN model is compared with the baselines through experiments, and the efficiency of the A-GSTCN model is verified by Jaccard, F1 and PRAUC. Not only that, the proposed model also reduces the training parameters by an order of magnitude.

Indexed as

dilated convolutionelectronic health recordsgraph structural-temporal convolutional networkmedication recommendation

Identifiers

PMID38002365

DOI10.3390/bioengineering10111241

PMCPMC10669740

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.