Nuanced Differences, Profound Impact: A Comparative Learning-Enhanced Knowledge Graph Recommender for Expert Identification in Specialized Medical Fields

The increasing specialization and segmentation of modern medical practice, while improving expertise, pose significant challenges in efficiently connecting patients with the right healthcare professionals. The vast array of medical specializations, coupled with sparse data on doctor profiles, overwhelms traditional recommendation algorithms. This study introduces CLEAR-Med: A Contrastive Learning Enhanced knowledge grAph Recommender designed to match patients with healthcare providers in specific Medical subfields. CLEAR-Med leverages a domain-specific knowledge graph (KG) and advanced contrastive learning (CL) techniques to capture the nuanced expertise and preferences of doctors, effectively addressing data sparsity and information overload in online healthcare communities (OHCs). The system constructs a comprehensive KG enriched with diverse information, including doctors’ social relationships, professional networks, and specialized attributes derived from OHC data. By embedding key entities and attributes through CL, CLEAR-Med generates robust representations, supported by a flexible attribute encoding module that integrates both efficient LSTMs and powerful Transformer-based models. Its modular prediction layer, featuring options from a stable MLP to an advanced generative diffusion model, then produces highly accurate and personalized recommendation sequences. CLEAR-Med demonstrates superior recommendation performance in baseline comparison experiments, excelling in adaptability and accuracy within OHC settings. Ablation studies confirm the effectiveness of individual components, while further experiments exploring advanced architectures like Transformers and diffusion models highlight the strong balance our framework strikes between performance and computational efficiency. Beyond addressing data sparsity challenges, CLEAR-Med establishes a strong foundation for future advancements in specialized medical matching systems, filling critical research gaps in the domain.