Abstract
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.
Abstract (translated)
药物通常会对日常患者的活动施加时间限制。违反这些医疗时间限制(MTCs)会导致治疗不遵守,同时导致不良健康结果和增加医疗费用。这些MTCs不仅在患者教育材料和临床文本中存在,还在药物使用指南(DUGs)中存在。通过计算在DUGs中表示MTCs的方法,可以推动以患者为中心的医疗应用程序的发展,帮助定义安全的患者活动模式。我们定义了一种新的MTC分类法,并开发了一种新的无上下文语法(CFG)模型,以计算在无结构DUGs中表示的MTCs。此外,我们发布了三个新的数据集,总大小为N = 836,其中每个数据集中都有规范化的MTCs标签。我们开发了自动从DUGs中获取和规范化MTCs的上下文学习解决方案,在所有数据集中的平均F1得分为0.62。最后,我们严格研究了ICL模型相对于基准模型的性能,包括数据集和MTC类型的基准模型,并进行深入的错误分析。
URL
https://arxiv.org/abs/2303.09366
