A deep learning approach integrating multi-dimensional features for expert matching in healthcare question answering communities.

ABSTRACT: To address the demand for precise patient-medical expert matching in online healthcare Q&A communities, this study proposes a multi-feature health community expert recommendation model integrating GRU, convolutional neural networks (CNN), and attention mechanisms. By analyzing textual semantic features from patients' question titles, content, tags and personal profiles, while incorporating medical experts' professional credentials information and historical reply sequences, we construct a recommendation framework with multi-dimensional feature fusion. The CNN model extracts deep semantic information from patient inquiries, coupled with a bidirectional GRU network to align with experts' specialized medical domains, thereby optimizing recommendation accuracy and relevance. Experimental resul

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12571844 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature