RTGN: Robust Traditional Chinese Medicine Graph Networks for Patient Similarity Learning

Traditional Chinese Medicine (TCM) boasts a long history and a unique diagnostic and therapeutic paradigm. Integrating TCM with Western medicine and modern medical devices has yielded numerous successful cases in recent years. TCM treatment has developed a special knowledge framework focusing on precise differentiation based on multidimensional information such as the patient's diseases, symptoms, and syndromes. This offers significant opportunities for AI research in similar patient scenarios within TCM contexts. However, traditional medicine's reliance on the physiological sensory judgment of human physicians to gather clinical information might lead to non-standardized descriptions and disturbances in patient assessments. Additionally, how to integrate TCM's fine-grained differentiation knowledge to design a patient similarity measure remains an open question. To address this, we first constructed a real-world dataset of TCM gastrointestinal malignancies (TCMGI) based on real cases in the Guang'anmen Hospital, China Academy of Chinese Medical Sciences. It contains 406 types of multidimensional information from 719 patients, organized in a graph structure. Second, we develop a novel deep learning framework, Robust Traditional Chinese Medicine Graph Networks (RTGN), which employs a Siamese network architecture with self-attention and self-supervision strategies to enhance robustness in patient retrieval. Lastly, we design a patient similarity metric integrating TCM and Western medicine approaches, demonstrating superior performance in depicting fine-grained patient similarities. Experimental results show our method outperforms existing best practices in patient retrieval accuracy. Moreover, the proposed similarity metric exhibits excellent performance in clustering tasks at various granularity levels, possibly supporting precision TCM patient retrieval and downstream tasks, such as prescription generation.