Hypergraph Contrastive Learning for Electronic Health Records

Abstract

Electronic Health Records (EHR) is the repository of patients' involved medical codes in the hospital, including diagnosis codes, medication codes, procedure codes, lab codes, and so on. EHR inherently contains various kinds of relationships such as the code-code, the patient-patient, and the patient-code relationship. Recent research shows that graph representation learning can be an effective tool for capturing complex relationships. However, none of the existing methods considered high-order interactions between patients and medical codes or considered the three relationships together. In this paper, we propose Hypergraph Contrastive Learning (HCL), to jointly learn patient embeddings and code embeddings from the combination of the above three relationships. HCL first constructs a hypergraph from the EHR data. Then, the medical code graph and the patient graph are constructed based on the hypergraph. Empowered with hypergraph attention network, Transformer, and graph attention network, HCL learns representations from three graphs respectively. Next, contrastive learning is applied to aggregate information from these graphs. Finally, the learned representations can support downstream tasks in supervised learning settings and self-supervised learning settings. Experiments are conducted on eICU and MIMIC-III datasets with mortality prediction and readmission prediction tasks. Results show that our method outperforms almost all compared methods on all evaluation metrics and HCL can learn patient representations from medical codes even without labeled data.

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