论文标题
从EHR中的药物指令中提取每日剂量：一种自动化的方法和经验教训
Extracting Daily Dosage from Medication Instructions in EHRs: An Automated Approach and Lessons Learned
论文作者
论文摘要
药物时间表已被证明可以有效地帮助医生可视化复杂的患者药物信息。许多这样设计的关键特征是对药物的日常剂量及其随着时间的变化的纵向表示。但是，通常未提供每日剂量作为离散值，需要从免费文本说明（SIG）中得出。每日剂量提取的现有作品的范围狭窄，旨在从临床注意事项中提取单一药物的剂量提取。在这里，我们提出了一种自动化方法来计算所有药物的每日剂量，将基于深度学习的命名实体提取器与词典词典和正则表达式相结合，在1,000个Sigs的专家生成的数据集中获得了0.98的精度和0.95召回。我们还分析了我们的专家生成的数据集，讨论了解SIG中包含的复杂信息的挑战，并提供见解，以指导通用每日剂量计算任务中未来的工作。
Medication timelines have been shown to be effective in helping physicians visualize complex patient medication information. A key feature in many such designs is a longitudinal representation of a medication's daily dosage and its changes over time. However, daily dosage as a discrete value is generally not provided and needs to be derived from free text instructions (Sig). Existing works in daily dosage extraction are narrow in scope, targeting dosage extraction for a single drug from clinical notes. Here, we present an automated approach to calculate daily dosage for all medications, combining deep learning-based named entity extractor with lexicon dictionaries and regular expressions, achieving 0.98 precision and 0.95 recall on an expert-generated dataset of 1,000 Sigs. We also analyze our expert-generated dataset, discuss the challenges in understanding the complex information contained in Sigs, and provide insights to guide future work in the general-purpose daily dosage calculation task.
