A Knowledge Graph-Based Intelligent Q&A System for Rare Diseases.

This study develops a knowledge graph-based intelligent Q&A system for rare diseases, integrating data from 126 diseases, 2,609 symptoms, and related departments. The Bert-BiLSTM-CRF model achieved 82.13% accuracy in entity extraction, and TextCNN achieved 94.