Similar patient retrieval is a fundamental task in medical informatics, aiming to identify patients with similar clinical characteristics to assist in diagnosis and treatment plan recommendation. While traditional methods relying on lexical features or medical ontologies often fail to capture implicit semantic relationships, recent advancements in dense retrieval methods powered by deep learning have shown promise yet face challenges in adapting to specific tasks such as similar patient retrieval. To address these limitations, we propose a medical knowledge-driven contrastive learning approach to enhance the representation capacity of general-purpose embedding models for medical text. Specifically, our approach introduces a novel negative sampling strategy leveraging International Classification of Diseases (ICD) codes to identify hard negatives. However, due to data imbalance issues, this method struggles to adequately mine negative examples. To overcome this limitation, we develop an external knowledge-based negative sampling method that incorporates both statistical and ambiguous knowledge, thereby enhancing the model's ability to differentiate between fine-grained medical conditions and complex clinical scenarios. We then integrate these methods into a contrastive learning framework to train more robust patient representations. Extensive experiments on real-world medical datasets show that our proposed method achieves significant improvements over existing state-of-the-art baseline models.
Meng et al. (Thu,) studied this question.