Conway Mike, Keyhani Salomeh, Christensen Lee, South Brett R, Vali Marzieh, Walter Louise C, Mowery Danielle L, Abdelrahman Samir, Chapman Wendy W
Department of Biomedical Informatics, 421 Wakara Way, University of Utah, alt Lake City, 84108, UT, USA.
San Francisco VA Medical Center, 4150 Clement Street, San Francisco, 94121, CA, USA.
J Biomed Semantics. 2019 Apr 11;10(1):6. doi: 10.1186/s13326-019-0198-0.
Social risk factors are important dimensions of health and are linked to access to care, quality of life, health outcomes and life expectancy. However, in the Electronic Health Record, data related to many social risk factors are primarily recorded in free-text clinical notes, rather than as more readily computable structured data, and hence cannot currently be easily incorporated into automated assessments of health. In this paper, we present Moonstone, a new, highly configurable rule-based clinical natural language processing system designed to automatically extract information that requires inferencing from clinical notes. Our initial use case for the tool is focused on the automatic extraction of social risk factor information - in this case, housing situation, living alone, and social support - from clinical notes. Nursing notes, social work notes, emergency room physician notes, primary care notes, hospital admission notes, and discharge summaries, all derived from the Veterans Health Administration, were used for algorithm development and evaluation.
An evaluation of Moonstone demonstrated that the system is highly accurate in extracting and classifying the three variables of interest (housing situation, living alone, and social support). The system achieved positive predictive value (i.e. precision) scores ranging from 0.66 (homeless/marginally housed) to 0.98 (lives at home/not homeless), accuracy scores ranging from 0.63 (lives in facility) to 0.95 (lives alone), and sensitivity (i.e. recall) scores ranging from 0.75 (lives in facility) to 0.97 (lives alone).
The Moonstone system is - to the best of our knowledge - the first freely available, open source natural language processing system designed to extract social risk factors from clinical text with good (lives in facility) to excellent (lives alone) performance. Although developed with the social risk factor identification task in mind, Moonstone provides a powerful tool to address a range of clinical natural language processing tasks, especially those tasks that require nuanced linguistic processing in conjunction with inference capabilities.
社会风险因素是健康的重要维度，与医疗服务可及性、生活质量、健康结局和预期寿命相关。然而，在电子健康记录中，许多社会风险因素的数据主要记录在自由文本临床笔记中，而非更易于计算的结构化数据，因此目前无法轻松纳入健康自动评估中。在本文中，我们介绍了Moonstone，这是一个全新的、高度可配置的基于规则的临床自然语言处理系统，旨在自动从临床笔记中提取需要推理的信息。我们该工具的初始用例聚焦于从临床笔记中自动提取社会风险因素信息——在此案例中为住房情况、独居情况和社会支持。源自退伍军人健康管理局的护理笔记、社会工作笔记、急诊室医生笔记、初级保健笔记、医院入院笔记和出院小结均用于算法开发和评估。
对Moonstone的评估表明，该系统在提取和分类三个感兴趣的变量（住房情况、独居情况和社会支持）方面具有高度准确性。该系统的阳性预测值（即精确率）得分范围为0.66（无家可归/住房条件差）至0.98（居家生活/非无家可归），准确率得分范围为0.63（居住在机构中）至0.95（独居），敏感度（即召回率）得分范围为0.75（居住在机构中）至0.97（独居）。
据我们所知，Moonstone系统是首个免费可用的开源自然语言处理系统，旨在从临床文本中提取社会风险因素，性能良好（居住在机构中）至优异（独居）。尽管Moonstone是为社会风险因素识别任务而开发的，但它提供了一个强大的工具来处理一系列临床自然语言处理任务，尤其是那些需要细致语言处理并结合推理能力的任务。
