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.

翻译：药物通常对患者的日常活动施加时间约束。违反此类医学时间约束（MTC）会导致治疗依从性不足，同时引发不良健康后果并增加医疗费用。这些MTC存在于患者教育材料和临床文本中的药物使用指南（DUG）中。通过计算方式表征DUG中的MTC，有助于定义安全的活动模式，从而推动以患者为中心的医疗应用发展。我们提出了一种针对DUG中MTC的新型分类体系，并开发了基于上下文无关文法（CFG）的计算模型，用于从非结构化DUG中表征MTC。此外，我们发布了三个新数据集，总计包含N=836个标注了标准化MTC的DUG。我们提出了一种基于上下文学习（ICL）的解决方案，用于自动提取和标准化DUG中的MTC，在所有数据集上的平均F1分数达到0.62。最后，我们通过跨数据集、跨MTC类型以及深度错误分析，系统性地研究了ICL模型相对于基准模型的性能表现。