上下文学习在医学时间约束提取中的适用范围
The Scope of In-Context Learning for the Extraction of Medical Temporal Constraints
AI总结：
本文提出医学时间约束分类体系与基于上下文无关文法的表示模型，并开发上下文学习方法自动提取药物指南中的时间约束，平均F1达0.62。
摘要：
药物通常会对患者的日常活动施加时间约束。违反此类医学时间约束（MTCs）会导致治疗依从性不足，同时还会带来不良的健康结局和增加的医疗费用。这些MTCs存在于药物使用指南（DUGs）中，既出现在患者教育材料中，也出现在临床文本中。以计算方式表示DUGs中的MTCs，将有助于通过帮助定义安全的患者活动模式来推进以患者为中心的医疗应用。我们定义了一个新的DUGs中MTCs的分类体系，并开发了一种基于上下文无关文法（CFG）的新模型，以从非结构化的DUGs中计算性地表示MTCs。此外，我们发布了三个新数据集，共包含N=836个标注了规范化MTCs的DUGs。我们开发了一种上下文学习（ICL）解决方案，用于自动提取和规范化DUGs中的MTCs，在所有数据集上实现了平均F1分数0.62。最后，我们严格地考察了ICL模型与基线模型在不同数据集和MTC类型上的性能对比，并进行了深入的错误分析。
英文摘要：
Medications often impose temporal constraints on everyday patient activity. Violations of such medical temporal constraints (MTCs) lead to a lack of treatment adherence, in addition to poor health outcomes and increased healthcare expenses. These MTCs are found in drug usage guidelines (DUGs) in both patient education materials and clinical texts. Computationally representing MTCs in DUGs will advance patient-centric healthcare applications by helping to define safe patient activity patterns. We define a novel taxonomy of MTCs found in DUGs and develop a novel context-free grammar (CFG) based model to computationally represent MTCs from unstructured DUGs. Additionally, we release three new datasets with a combined total of N = 836 DUGs labeled with normalized MTCs. We develop an in-context learning (ICL) solution for automatically extracting and normalizing MTCs found in DUGs, achieving an average F1 score of 0.62 across all datasets. Finally, we rigorously investigate ICL model performance against a baseline model, across datasets and MTC types, and through in-depth error analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
