Exploring a Big Data-Driven Dialogue Generation Method for Disease Queries Using RASA

As machine learning technologies continue to ad-vance and natural language processing systems become in-creasingly sophisticated, conversational systems are becoming more prevalent across various domains. Simultaneously, the improvement of healthcare systems has made it possible to develop specialized medical task-oriented conversational systems. Against this backdrop, this project builds a big data-driven conversational generation system focused on disease queries, it is based on a Chinese healthcare database and uses the Rasa framework. The system aims to extract information required for constructing a medical conversational system from big data, covering queries related to disease basic descriptions, causes, symptoms, recommended foods, dietary restrictions, treatment methods, medications, examination items, susceptible populations, and preventive measures. The system's effectiveness is validated using a combined subjective and objective evaluation approach, offering users a personalized online querying platform. By leveraging big data to construct a medical conversational generation system, this project elevates the system's intelligence level and establishes a paradigm for constructing conversational systems using big data. It aims to provide users with more comprehensive and precise medical services.