Use of Natural Language Processing Tools to Identify and Classify Periprosthetic Femur Fractures
Use of Natural Language Processing Tools to Identify and Classify Periprosthetic Femur Fractures
复制标题
DOI：
10.1016/j.arth.2019.07.025
复制
发表时间：
2019-10-01
影响因子：
3.5
通讯作者：
Kremers, Hilal Maradit
中科院分区：
文献类型：
--
作者：
Tibbo, Meagan E.;Wyles, Cody C.;Kremers, Hilal Maradit
背景：人工病历审查是劳动密集型的，需要训练有素的医疗专业人员拥有的专业知识。实现这一点所需的成本和基础设施挑战对大多数医院来说是令人望而却步的。自然语言处理（NLP）工具在从电子健康记录中的非结构化文本中提取关键信息的能力方面与众不同。作为NLP技术在全髋关节置换术（THA）中潜在应用的简单概念验证，我们检查了其识别假体周围股骨骨折（PPFFx）的能力，然后进行更复杂的温哥华classification.Methods：在1998年至2016年期间在一个学术机构进行的所有THA中识别PPFFx。随机选择的训练队列（1538例THA，89例PPFFx病例）用于开发原型NLP算法，另外随机选择的队列（2982例THA，84例PPFFx病例）用于进一步验证算法。对THA的关键词进行识别和分类，定义THA的温哥华型PPFFx。金标准由经验丰富的整形外科医生使用图表和放射学检查进行确认。该算法应用于咨询和手术记录，以评价外科医生使用的语言，作为在没有列出的精确诊断的情况下预测正确病理的手段。鉴于不同外科医生对骨折描述的固有可变性，在错误识别后的训练阶段，使用迭代过程来改进算法。验证统计计算使用手动图表审查作为金standard.Results：在区分PPFFx，NLP算法表现出100%的灵敏度和99.8%的特异性。在84 PPFFx测试的情况下，该算法表现出78.6%的灵敏度和94.8%的特异性，在确定正确的温哥华classification.Conclusion：NLP启用的算法是一个有前途的替代手动图表审查确定THA的结果。应用于外科医生记录的NLP算法在描绘PPFFx方面表现出极好的准确性，但温哥华分类亚型的准确性较低。该概念验证研究支持使用NLP技术以快速且具有成本效益的方式从电子健康记录中的非结构化文本中提取THA特定数据元素。(C)2019爱思唯尔公司All rights reserved.
Background: Manual chart review is labor-intensive and requires specialized knowledge possessed by highly trained medical professionals. The cost and infrastructure challenges required to implement this is prohibitive for most hospitals. Natural language processing (NLP) tools are distinctive in their ability to extract critical information from unstructured text in the electronic health records. As a simple proof-ofconcept for the potential application of NLP technology in total hip arthroplasty (THA), we examined its ability to identify periprosthetic femur fractures (PPFFx) followed by more complex Vancouver classification.Methods: PPFFx were identified among all THAs performed at a single academic institution between 1998 and 2016. A randomly selected training cohort (1538 THAs with 89 PPFFx cases) was used to develop the prototype NLP algorithm and an additional randomly selected cohort (2982 THAs with 84 PPFFx cases) was used to further validate the algorithm. Keywords to identify, and subsequently classify, Vancouver type PPFFx about THA were defined. The gold standard was confirmed by experienced orthopedic surgeons using chart and radiographic review. The algorithm was applied to consult and operative notes to evaluate language used by surgeons as a means to predict the correct pathology in the absence of a listed, precise diagnosis. Given the variability inherent to fracture descriptions by different surgeons, an iterative process was used to improve the algorithm during the training phase following error identification. Validation statistics were calculated using manual chart review as the gold standard.Results: In distinguishing PPFFx, the NLP algorithm demonstrated 100% sensitivity and 99.8% specificity. Among 84 PPFFx test cases, the algorithm demonstrated 78.6% sensitivity and 94.8% specificity in determining the correct Vancouver classification.Conclusion: NLP-enabled algorithms are a promising alternative to manual chart review for identifying THA outcomes. NLP algorithms applied to surgeon notes demonstrated excellent accuracy in delineating PPFFx, but accuracy was low for Vancouver classification subtype. This proof-of-concept study supports the use of NLP technology to extract THA-specific data elements from the unstructured text in electronic health records in an expeditious and cost-effective manner. (C) 2019 Elsevier Inc. All rights reserved.