A Doctor Recommendation Method in Telemedicine Based on Multi-granularity Features Extraction

Developing doctor recommendation techniques has the potential to enhance the efficiency of telemedicine service with the increasing demand for telemedicine. We propose a novel recommendation method tailored for more sparser and more professional telemedicine contexts than online healthcare. Firstly, we construct a knowledge graph based on the expertise of physicians to extract the feature of disease relevance, so as to make up for the sparsity of data. Subsequently, coarse and fine granularity semantic feature is extracted from historical diagnostic data to calculate text similarity between doctors and patients. Then, the features of gender, age, title and scheduling activity are considered to improve model performance doctor modeling. Finally, we input the extracted features into a neural network to generate recommendation results that are both effective and interpretable. Experimental results demonstrate that, compared to traditional methods, our approach significantly improves the accuracy and robustness of telemedicine doctor recommendations. Additionally, interpretability analysis shows text similarity and disease relevance (obtained from doctors' professional expertise and consultation text) contribute mostly to the recommendation system, which reconfirms our efforts are meaningful.