TY - GEN
T1 - Exploring a Big Data-Driven Dialogue Generation Method for Disease Queries Using RASA
AU - Quan, Xinyue
AU - Dai, Xiaohan
AU - Xie, Xiang
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - big data driven dialogue system
KW - disease Inquiry
KW - medical dialogue system
KW - RASA
KW - task-oriented dialogue system
UR - http://www.scopus.com/inward/record.url?scp=85201733655&partnerID=8YFLogxK
U2 - 10.1109/ICBDA61153.2024.10607321
DO - 10.1109/ICBDA61153.2024.10607321
M3 - Conference contribution
AN - SCOPUS:85201733655
T3 - 2024 9th International Conference on Big Data Analytics, ICBDA 2024
SP - 316
EP - 321
BT - 2024 9th International Conference on Big Data Analytics, ICBDA 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Big Data Analytics, ICBDA 2024
Y2 - 16 March 2024 through 18 March 2024
ER -