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.

我们介绍了 RJUA-QA，这是一个新颖的医学数据集，用于问题回答和与临床证据推理，有助于弥合通用大型语言模型（LLMs）与医学特定 LLM 应用之间的差距。

RJUA-QA：一份完整的泌尿科问答数据集

RJUA-QA: A Comprehensive QA Dataset for Urology

Clinical evidence encompasses the associations and impacts between patients,
interventions (such as drugs or physiotherapy), problems, and outcomes. The
goal of recommending clinical evidence is to provide medical practitioners with
relevant information to support their decision-making processes and to generate
new evidence. Our specific task focuses on recommending evidence based on
clinical problems. However, the direct connections between certain clinical
problems and related evidence are often sparse, creating a challenge of link
sparsity. Additionally, to recommend appropriate evidence, it is essential to
jointly exploit both topological relationships among evidence and textual
information describing them. To address these challenges, we define two
knowledge graphs: an Evidence Co-reference Graph and an Evidence Text Graph, to
represent the topological and linguistic relations among evidential elements,
respectively. We also introduce a multi-channel heterogeneous learning model
and a fusional attention mechanism to handle the co-reference-text
heterogeneity in evidence recommendation. Our experiments demonstrate that our
model outperforms state-of-the-art methods on open data.

该研究旨在针对临床问题推荐临床证据，为此，研究者们定义了支持临床证据推荐的两个知识图谱：证据共指图和证据文本图，同时提出了一种多通道异质性学习模型和融合关注机制来解决证据推荐中的异构性问题，并在实验中取得了优于现有方法的效果。

利用证据图的多通道异构学习增强临床证据推荐

Enhancing Clinical Evidence Recommendation with Multi-Channel  Heterogeneous Learning on Evidence Graphs

Clinical trials provide essential guidance for practicing Evidence-Based
Medicine, though often accompanying with unendurable costs and risks. To
optimize the design of clinical trials, we introduce a novel Clinical Trial
Result Prediction (CTRP) task. In the CTRP framework, a model takes a
PICO-formatted clinical trial proposal with its background as input and
predicts the result, i.e. how the Intervention group compares with the
Comparison group in terms of the measured Outcome in the studied Population.
While structured clinical evidence is prohibitively expensive for manual
collection, we exploit large-scale unstructured sentences from medical
literature that implicitly contain PICOs and results as evidence. Specifically,
we pre-train a model to predict the disentangled results from such implicit
evidence and fine-tune the model with limited data on the downstream datasets.
Experiments on the benchmark Evidence Integration dataset show that the
proposed model outperforms the baselines by large margins, e.g., with a 10.7%
relative gain over BioBERT in macro-F1. Moreover, the performance improvement
is also validated on another dataset composed of clinical trials related to
COVID-19.

为了优化临床试验的设计，我们提出了一种新的临床试验结果预测（CTRP）任务，并利用大规模非结构化结构的医学文献中的语句隐含地包含 PICO 和结果的证据，预训练模型在下游数据集上进行微调得到更好的表现，针对 COVID-19 的另一个数据集也验证了性能提升。