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- Title
Disease Diagnosis Based on Multi-View Contrastive Learning for Electronic Medical Records.
- Authors
Zhengkang Zhang; Dan Yang; Yu Zhang
- Abstract
Disease diagnosis based on electronic medical records (EMRs) is one of the important research contents of intelligent healthcare. In recent years, disease diagnosis based on heterogeneous graph neural networks has received increasing attention. However, disease diagnosis tasks often suffer from the lack of labeling information due to the high cost of manual labeling. And existing disease diagnosis models based on heterogeneous graph neural networks ignore the correlation between different meta-paths. Therefore, we propose a disease diagnosis model based on multi-view contrastive learning (MVCDD). MVCDD uses medical data from electronic medical records to construct medical heterogeneous graphs, and uses a fixed-depth random walk method to obtain semantic subgraphs defined by multiple meta-paths. Meanwhile, we introduce the inter-view contrastive learning task to model the correlation between different meta-paths. MVCDD optimizes the patient representations by combining intra-view and inter-view contrastive learning tasks jointly. Extensive experiments are conducted on the MIMIC-III dataset. The experimental results on the MIMIC-III dataset demonstrate that MVCDD outperforms other baselines and effectively improves the performance of disease diagnosis.
- Subjects
ELECTRONIC health records; DIGITAL learning; DIAGNOSIS; RANDOM walks; FAULT diagnosis
- Publication
IAENG International Journal of Applied Mathematics, 2023, Vol 53, Issue 3, p1114
- ISSN
1992-9978
- Publication type
Article