We motivate and introduce CHARD: Clinical Health-Aware Reasoning across
Dimensions, to investigate the capability of text generation models to act as
implicit clinical knowledge bases and generate free-flow textual explanations
about various health-related conditions across several dimensions. We collect
and present an associated dataset, CHARDat, consisting of explanations about 52
health conditions across three clinical dimensions. We conduct extensive
experiments using BART and T5 along with data augmentation, and perform
automatic, human, and qualitative analyses. We show that while our models can
perform decently, CHARD is very challenging with strong potential for further
exploration.

介绍了 CHARD：跨尺度临床健康知识驱动的推理模型，利用文本生成模型生成关于多种健康相关情况的多维自由文本说明。通过使用 BART 和 T5 进行数据增强，在自动、人工和定性分析方面进行广泛实验，提出了一个 52 种健康问题的说明数据集：CHARDat，显示该模型可以表现不俗，但仍存在深入研究的巨大潜力。