Automated Extraction of VTE Events From Narrative Radiology Reports in Electronic Health Records: A Validation Study.
Automated Extraction of VTE Events From Narrative Radiology Reports in Electronic Health Records: A Validation Study.
复制标题
DOI：
10.1097/mlr.0000000000000346
复制
发表时间：
2017-10
期刊：
影响因子：
3
通讯作者：
Rochefort CM
中科院分区：
文献类型：
--
作者：
Tian Z;Sun S;Eguale T;Rochefort CM
静脉血栓栓塞（VTE）的监测是必要的，以提高病人的安全性，在急性护理医院，但目前的检测方法是不准确和效率低下。随着电子格式的临床叙述越来越多，使用自然语言处理（NLP）技术的自动监测可能是一种更好的方法。我们评估了使用符号NLP从放射学报告中识别VTE的2种临床表现（深静脉血栓形成（DVT）和肺栓塞（PE））的准确性。在2008年至2012年期间在蒙特利尔（加拿大）5家成人护理医院的大学健康网络中进行的可诊断DVT或PE的成像研究中随机选择了4000份叙述性报告。这些报告由临床专家编码，以识别DVT和PE的阳性和阴性病例，作为参考标准。使用来自最大医院（n=2788）的数据，训练了2个符号NLP分类器;一个用于DVT，另一个用于PE。这些分类器的准确性进行了测试的数据从其他4家医院（n=1212）。在手动审查时，确定了663份DVT阳性和272份PE阳性报告。在测试数据集中，DVT分类器实现了94%的灵敏度（95% CI，88%-97%），96%特异性（95% CI，94%-97%）和73%阳性预测值（95% CI，65%-80%），而PE分类器实现了94%的灵敏度（95% CI，89%-97%），96%特异性（95% CI，95%-97%）和80%阳性预测值（95% CI，73%-85%）。符号NLP可以准确地从叙述性放射学报告中识别VTE。该方法可用于静脉血栓栓塞症的监测和预防措施的评价。
Surveillance of venous thromboembolisms (VTEs) is necessary for improving patient safety in acute care hospitals, but current detection methods are inaccurate and inefficient. With the growing availability of clinical narratives in an electronic format, automated surveillance using natural language processing (NLP) techniques may represent a better method. We assessed the accuracy of using symbolic NLP for identifying the 2 clinical manifestations of VTE, deep vein thrombosis (DVT) and pulmonary embolism (PE), from narrative radiology reports. A random sample of 4000 narrative reports was selected among imaging studies that could diagnose DVT or PE, and that were performed between 2008 and 2012 in a university health network of 5 adult-care hospitals in Montreal (Canada). The reports were coded by clinical experts to identify positive and negative cases of DVT and PE, which served as the reference standard. Using data from the largest hospital (n=2788), 2 symbolic NLP classifiers were trained; one for DVT, the other for PE. The accuracy of these classifiers was tested on data from the other 4 hospitals (n=1212). On manual review, 663 DVT-positive and 272 PE-positive reports were identified. In the testing dataset, the DVT classifier achieved 94% sensitivity (95% CI, 88%-97%), 96% specificity (95% CI, 94%-97%), and 73% positive predictive value (95% CI, 65%-80%), whereas the PE classifier achieved 94% sensitivity (95% CI, 89%-97%), 96% specificity (95% CI, 95%-97%), and 80% positive predictive value (95% CI, 73%-85%). Symbolic NLP can accurately identify VTEs from narrative radiology reports. This method could facilitate VTE surveillance and the evaluation of preventive measures.