RJUA-QA: A Comprehensive QA Dataset for Urology

Shiwei Lyu; Chenfei Chi; Hongbo Cai; Lei Shi; Xiaoyan Yang; Lei Liu; Xiang Chen; Deng Zhao; Zhiqiang Zhang; Xianguo Lyu; Ming Zhang; Fangzhou Li; Xiaowei Ma; Yue Shen; Jinjie Gu; Wei Xue; Yiran Huang

arXiv · 2312.09785

RJUA-QA: A Comprehensive QA Dataset for Urology

Shiwei Lyu·Chenfei Chi·Hongbo Cai·Lei Shi·Xiaoyan Yang·Lei Liu·Xiang Chen·Deng Zhao·Zhiqiang Zhang·Xianguo Lyu·Ming Zhang·Fangzhou Li·Xiaowei Ma·Yue Shen·Jinjie Gu·Wei Xue·Yiran Huang

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Abstract

We introduce RJUA-QA, a novel medical dataset for question answering (QA) and reasoning with clinical evidence, contributing to bridge the gap between general large language models (LLMs) and medical-specific LLM applications. RJUA-QA is derived from realistic clinical scenarios and aims to facilitate LLMs in generating reliable diagnostic and advice. The dataset contains 2,132 curated Question-Context-Answer pairs, corresponding about 25,000 diagnostic records and clinical cases. The dataset covers 67 common urological disease categories, where the disease coverage exceeds 97.6\% of the population seeking medical services in urology. Each data instance in RJUA-QA comprises: (1) a question mirroring real patient to inquiry about clinical symptoms and medical conditions, (2) a context including comprehensive expert knowledge, serving as a reference for medical examination and diagnosis, (3) a doctor response offering the diagnostic conclusion and suggested examination guidance, (4) a diagnosed clinical disease as the recommended diagnostic outcome, and (5) clinical advice providing recommendations for medical examination. RJUA-QA is the first medical QA dataset for clinical reasoning over the patient inquiries, where expert-level knowledge and experience are required for yielding diagnostic conclusions and medical examination advice. A comprehensive evaluation is conducted to evaluate the performance of both medical-specific and general LLMs on the RJUA-QA dataset. Our data is are publicly available at \url{https://github.com/alipay/RJU_Ant_QA}.

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Shiwei Lyu, Chenfei Chi, Hongbo Cai, Lei Shi, Xiaoyan Yang, Lei Liu, Xiang Chen, Deng Zhao, Zhiqiang Zhang, Xianguo Lyu, Ming Zhang, Fangzhou Li, Xiaowei Ma, Yue Shen, Jinjie Gu, Wei Xue, Yiran Huang. 2023-12-15. RJUA-QA: A Comprehensive QA Dataset for Urology. https://arxiv.org/abs/2312.09785

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