Recommending physicians with multimodal data and medical knowledge graph on healthcare platforms

Abstract

Healthcare platforms have attracted many physicians and provided convenient medical services to patients. However, the large number of physicians brings the difficulty of finding suitable physicians for the patients. Despite attempts to develop recommendation methods to address this challenge, they fail to leverage multimodal medical data, which contain numerical, categorical, textual and visual data valuable for inferring patients’ preferences for physicians. Besides, previous methods ignore the semantic gap between patients’ health conditions and physicians’ specialties. The conditions describe the patients’ symptoms, while the specialties indicate the diseases the physicians can treat. They have different vocabularies and cannot be directly compared for generating recommendations. We put forward an innovative physician recommendation approach to effectively address the above research gaps. Our approach entails merging multimodal data with multiple network modules and employing a medical knowledge graph to fill the semantic gap. To assess the validity of our suggested approach, we perform comprehensive trials on real-world data. The trial outcomes indicate that our approach surpasses its variants and existing methods in the aspects of HR@k, MRR@k and NDCG@k.