从病历中提取因果树，直观展现诊断推理过程。
Causal Tree Extraction from Medical Case Reports: A Novel Task for Experts-like Text Comprehension
- 将诊断过程建模为多层因果树，根节点为原发疾病。
- 构建日文病历因果树数据集J-Casemap，生成法提升20.2分。
- 适合医学文本理解、临床推理研究者使用。
从病历中提取因果关系对理解病例，特别是诊断过程至关重要。由于诊断过程属于自下而上的推理，病例中的因果关系自然形成多层树状结构。现有任务如医学关系抽取无法捕捉整个病例的因果关系，因它们平等地对待所有关系，忽略了诊断过程固有的层次结构。为此，我们提出新任务——因果树提取（CTE），输入病历报告，输出以主病变为根的因果树，直观呈现诊断推理流程。随后，我们构建了日文病历因果树数据集J-Casemap，提出基于生成的CTE方法，在人工评估中比基线高出20.2分，并引入反映临床偏好性的评估指标。进一步实验表明，J-Casemap还能提升其他医学任务（如问答）的性能。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。