Medical question answer (QA) assistants respond to lay users' health-related
queries by synthesizing information from multiple sources using natural
language processing and related techniques. They can serve as vital tools to
alleviate issues of misinformation, information overload, and complexity of
medical language, thus addressing lay users' information needs while reducing
the burden on healthcare professionals. QA systems, the engines of such
assistants, have typically used either language models (LMs) or knowledge
graphs (KG), though the approaches could be complementary. LM-based QA systems
excel at understanding complex questions and providing well-formed answers, but
are prone to factual mistakes. KG-based QA systems, which represent facts well,
are mostly limited to answering short-answer questions with pre-created
templates. While a few studies have jointly used LM and KG approaches for
text-based QA, this was done to answer multiple-choice questions. Extant QA
systems also have limitations in terms of automation and performance. We
address these challenges by designing a novel, automated disease QA system
which effectively utilizes both LM and KG techniques through a joint-reasoning
approach to answer disease-related questions appropriate for lay users. Our
evaluation of the system using a range of quality metrics demonstrates its
efficacy over benchmark systems, including the popular ChatGPT.

通过综合自然语言处理和相关技术从多个信息源中合成信息，医用问答（QA）助手能够回答非专业用户的健康相关问题，解决了错误信息、信息过载和医学语言复杂性等问题，并减轻了医护人员的负担。本研究通过联合推理策略充分利用语言模型和知识图谱技术，设计了一种新颖的自动疾病问答系统，用于回答适合非专业用户的疾病相关问题，并通过多种质量指标评估其效力，优于流行的 ChatGPT 等基准系统。