Abstract:

The rapid development of Internet medical treatment has greatly improved the convenience of seeking medical advice， and at the same time， it has also caused patients to face the difficulty of finding a suitable doctor from the massive doctor database. Effective doctor recommendation has become an important way to improve the experience of patients on the Internet medical platform. As it is related to life and health， compared with ordinary product and service recommendation， doctor recommendation has higher requirements for the reliability of results. Deep learning models have been widely used in personalized recommendations in recent years. However， the inherent uncertainty of the deep learning models can easily lead to uncertain recommendation results， thereby affecting the reliability of the doctor recommendation. To address this， the inherent uncertainty of deep learning models is considered and a Multi-Modal Bayesian Neural Collaborative Filtering model （MM-BNCF） is proposed based on Bayesian learning theory. First， a Bayesian deep learning model is constructed based on Monte Carlo Dropout， with structured patient feedback and unstructured doctor and patient text data as input， to obtain representations of doctors and patients separately. Second， the Bayesian deep model is used to analyze the compatibility between doctors and patients， and to assess the uncertainty of doctor-patient matching. Then， doctor recommendation results are generated based on compatibility. Final， experimental results based on real data from the haodf.com online platform validate that the proposed recommendation method in this paper can be more effectively applied to online doctor recommendation tasks. There is theoretical and practical significance for improving the performance and patient satisfaction of Internet medical websites in this study.

Key words: online doctor recommendation, deep learning uncertainty, bayesian deep learning, multi-model data, internet medical care