Moonstone: a novel natural language processing system for inferring social risk from clinical narratives
Moonstone: a novel natural language processing system for inferring social risk from clinical narratives
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DOI：
10.1186/s13326-019-0198-0
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发表时间：
2019-04-11
影响因子：
1.9
通讯作者：
Chapman, Wendy W.
中科院分区：
文献类型：
--
作者：
Conway, Mike;Keyhani, Salomeh;Chapman, Wendy W.
工作背景：社会风险因素是健康的重要方面，与获得护理、生活质量、健康结果和预期寿命有关。然而，在电子健康记录中，与许多社会风险因素相关的数据主要记录在自由文本临床笔记中，而不是更容易计算的结构化数据，因此目前无法轻松纳入自动健康评估。在本文中，我们提出了月亮石，一个新的，高度可配置的基于规则的临床自然语言处理系统，旨在自动提取信息，需要从临床笔记推断。我们对该工具的初始用例集中在从临床记录中自动提取社会风险因素信息-在这种情况下，住房情况，独居和社会支持。护理笔记，社会工作笔记，急诊室医生笔记，初级保健笔记，入院笔记，出院摘要，所有来自退伍军人健康管理局，被用于算法的开发和evaluation.Results：月长石的评价表明，该系统是高度准确的提取和分类的三个变量的利益（住房情况，独居，社会支持）。该系统达到了阳性预测值（即精确度）评分范围为0.66（无家可归者/边缘住房者）降至0.98（住在家里/不无家可归），准确性得分范围从0.63（住在设施内）至0.95（独自生活）和敏感性（即回忆）评分范围为0.75（住在设施内）至0.97结论：据我们所知，月光石系统是第一个免费提供的，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，具有良好（生活在设施中）到优秀（单独生活）的性能。虽然开发时考虑到了社会风险因素识别任务，但Moonstone提供了一个强大的工具来解决一系列临床自然语言处理任务，特别是那些需要细致入微的语言处理与推理能力相结合的任务。
Background: 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.Results: 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).Conclusions: 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.