MExplore: an entity-based visual analytics approach for medical expertise acquisition

Acquiring medical expertise is a critical component of medical education and professional development. While existing studies focus primarily on constructing medical knowledge bases or developing learning tools based on the structured, private healthcare data, they often lack methods for extracting expertise from unstructured medical texts. These texts constitute a significant portion of medical literature and offer greater flexibility and detail compared to structured data formats. Furthermore, many studies fail to provide explicit analytical and learning pathways in this context. This paper introduces MExplore, an interactive visual analytics system designed to support the acquisition of medical expertise. To address the challenges of the inconsistencies and confidentiality concerns inherent in unstructured medical texts, we propose a workflow that employs a fine-tuned BERT-based model to extract medical entities (MEs) from them. We then present a novel multilevel visual analysis framework that integrates multiple coordinated visualizations, enabling a progressive and interactive exploration of medical knowledge. To assess the effectiveness of MExplore, we conducted three case studies, a user study, and interviews with domain experts. The results indicate that the system significantly enhances the medical expertise acquisition process, providing an effective interactive approach for acquiring and retaining knowledge from medical texts.