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：我们提出并引入CHARD：跨维度临床健康感知推理，旨在探究文本生成模型作为隐式临床知识库的能力，并生成关于多种健康相关条件在多维度下的自由流畅文本解释。我们收集并呈现了相关数据集CHARDat，包含对52种健康状况在三个临床维度的解释。我们使用BART和T5结合数据增强进行了广泛实验，并开展了自动、人工和定性分析。结果表明，尽管我们的模型表现尚可，但CHARD具有极高挑战性，且蕴含着进一步探索的巨大潜力。