Abstract
Extracting causal relationships from a medical case report is essential for comprehending the case, particularly its diagnostic process. Since the diagnostic process is regarded as a bottom-up inference, causal relationships in cases naturally form a multi-layered tree structure. The existing tasks, such as medical relation extraction, are insufficient for capturing the causal relationships of an entire case, as they treat all relations equally without considering the hierarchical structure inherent in the diagnostic process. Thus, we propose a novel task, Causal Tree Extraction (CTE), which receives a case report and generates a causal tree with the primary disease as the root, providing an intuitive understanding of a case's diagnostic process. Subsequently, we construct a Japanese case report CTE dataset, J-Casemap, propose a generation-based CTE method that outperforms the baseline by 20.2 points in the human evaluation, and introduce evaluation metrics that reflect clinician preferences. Further experiments also show that J-Casemap enhances the performance of solving other medical tasks, such as question answering.
Abstract (translated)
从医疗案例报告中提取因果关系对于理解病例，尤其是其诊断过程至关重要。由于诊断过程被视为自下而上的推理方法，因此案例中的因果关系自然形成了多层次的树状结构。现有的任务，如医学关系抽取，不足以捕捉整个案例的因果关系，因为它们将所有关系同等看待，忽略了诊断过程中固有的层次结构。为此，我们提出了一项新的任务——因果树提取（CTE），该任务接收一个病例报告并生成以主要疾病为根节点的因果树，从而直观地展示出病例诊断过程。随后，我们构建了一个日本案例报告CTE数据集J-Casemap，并提出了基于生成的CTE方法，在人工评估中比基线高出20.2分，还引入了反映临床医生偏好的评价指标。进一步的实验也表明，J-Casemap能够增强解决其他医疗任务（如问答）的能力。
URL
https://arxiv.org/abs/2503.01302
