Intelligent Conversational Agent for Medical Information
Our paper, “Intelligent Conversational Agent for Medical Information”, has been published by Springer Nature, marking a significant achievement in the field of healthcare solutions. This research was presented at the 29th International Conference on Natural Language & Information Systems, held at the University of Turin, Italy, from June 25-27, 2024, showcasing our approach to improving medical information handling.
The medical information teams of pharmaceutical companies aim to provide reliable clinical and scientific information to healthcare professionals and patients, handling millions of inquiries annually through various channels like phone and email. To meet customer needs, they use standardized processes and computational solutions. These teams can improve operations through automation and are looking to use AI technologies with intelligent and cognitive capabilities.
In this paper we address the problem of automating the process of handling the numerous inquiries received by the medical information teams from the users in the pharmaceutical industry. Our approach foresees the development of a holistic system which includes an intelligent conversational agent that is informed by a set of questions and answers (Q&A), extracted from a large corpus of medical scientific documents in a semi-automatic manner. We investigate two different methods (i.e., template-based and neural-based) for extracting Q&A pairs that are subsequently used to train the Natural Language Understanding model of the conversational agent. Both methods are qualitatively evaluated by experts of a medical information team. The performance of our system shows that our approach is robust with promising results which can reach an average performance of 64%.
Find out more about the paper https://link.springer.com/chapter/10.1007/978-3-031-70242-6_32
Citation: Zeltsi, A. et al. (2024). Intelligent Conversational Agent for Medical Information. In: Rapp, A., Di Caro, L., Meziane, F., Sugumaran, V. (eds) Natural Language Processing and Information Systems. NLDB 2024. Lecture Notes in Computer Science, vol 14763. Springer, Cham. https://doi.org/10.1007/978-3-031-70242-6_32
Acknowledgements: We would like to thank Aleksandra Piekarczyk, Layalie Matouk, Amn Paracha, and Daniel Castillo Vaughan from Pfizer’s Medical Information team for their contribution to the evaluation of the agents.
Disclosure of Interests: This research was conducted as a collaboration between the Centre for Research & Technology Hellas and Pfizer. Pfizer is the research sponsor.
