University of North Carolina at Chapel Hill, USA.
University of Wisconsin-Madison, USA.
Health Informatics J. 2020 Mar;26(1):388-405. doi: 10.1177/1460458218824742. Epub 2019 Feb 22.
Lifestyle modification, including diet, exercise, and tobacco cessation, is the first-line treatment of many disorders including hypertension, obesity, and diabetes. Lifestyle modification data are not easily retrieved or used in research due to their textual nature. This study addresses this knowledge gap using natural language processing to automatically identify lifestyle modification documentation from electronic health records. Electronic health record notes from hypertension patients were analyzed using an open-source natural language processing tool to retrieve assessment and advice regarding lifestyle modification. These data were classified as lifestyle modification assessment or advice and mapped to a coded standard ontology. Combined lifestyle modification (advice and assessment) recall was 99.27 percent, precision 94.44 percent, and correct classification 88.15 percent. Through extraction and transformation of narrative lifestyle modification data to coded data, this critical information can be used in research, metric development, and quality improvement efforts regarding care delivery for multiple medical conditions that benefit from lifestyle modification.
生活方式的改变，包括饮食、运动和戒烟，是许多疾病（包括高血压、肥胖症和糖尿病）的一线治疗方法。由于生活方式改变数据的文本性质，它们在研究中不容易被检索或使用。本研究使用自然语言处理来自动识别电子健康记录中的生活方式改变文档，从而解决了这一知识空白。利用开源自然语言处理工具分析高血压患者的电子健康记录笔记，以检索关于生活方式改变的评估和建议。这些数据被分类为生活方式改变评估或建议，并映射到编码标准本体上。综合生活方式改变（建议和评估）召回率为 99.27%，精度为 94.44%，正确分类率为 88.15%。通过提取和转换叙述性的生活方式改变数据为编码数据，可以将这些关键信息用于与生活方式改变受益的多种医疗条件的护理提供相关的研究、指标制定和质量改进工作中。
