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.

翻译：我们鼓励并引入CHARD:跨维度临床健康知识,调查文本生成模型作为隐性临床知识库的能力,并针对不同层面的与健康有关的各种条件进行自由流文本解释;我们收集并提交一个相关的数据集,CHARDat, 包括三个临床层面的52个健康条件的解释;我们利用BART和T5进行广泛的实验,同时增加数据,并进行自动、人和定性分析;我们表明,虽然我们的模型能够很好地发挥功能,但CHARD具有巨大的进一步探索潜力,具有巨大的挑战性。