Naseem, UsmanThapa, SurendrabikramZhang, QiHu, LiangNasim, Mehwish2023-08-022023-08-022023-07-19http://hdl.handle.net/10919/115960Medical dialogue systems (MDS) have shown promising abilities to diagnose through a conversation with a patient like a human doctor would. However, current systems are mostly based on sequence modeling, which does not account for medical knowledge. This makes the systems more prone to misdiagnosis in case of diseases with limited information. To overcome this issue, we present MDKG, an end-to-end dialogue system for medical dialogue generation (MDG) specifically designed to adapt to new diseases by quickly learning and evolving a meta-knowledge graph that allows it to reason about disease-symptom correlations. Our approach relies on a medical knowledge graph to extract disease-symptom relationships and uses a dynamic graph-based meta-learning framework to learn how to evolve the given knowledge graph to reason about disease-symptom correlations. Our approach incorporates medical knowledge and hence reduces the need for a large number of dialogues. Evaluations show that our system outperforms existing approaches when tested on benchmark datasets.application/pdfenIn CopyrightArticle - Refereed2023-08-01The author(s)https://doi.org/10.1145/3539618.3592019