Classification of radiology reports for falls in an HIV study cohort
Classification of radiology reports for falls in an HIV study cohort
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DOI：
10.1093/jamia/ocv155
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发表时间：
2016-04-01
影响因子：
6.4
通讯作者：
Womack, Julie A.
中科院分区：
文献类型：
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作者：
Bates, Jonathan;Fodeh, Samah J.;Womack, Julie A.
方法我们使用退伍军人老龄化队列研究虚拟队列(VAS-VC)，这是一个基于电子健康记录的队列，包括146 530名有放射学报告的退伍军人(N=2 977 739)。我们建立了一个放射学报告的参考标准，每个报告用一个特征词集合和统一的医学语言系统概念来表示，然后开发了几个用于跌倒的支持向量机(SVM)分类器。我们比较了互信息(MI)排序和嵌入特征选择方法。结果基于MI特征选择的支持向量机分类器在测试集上达到了97.04的曲线下面积。当应用于VAS-VC中的所有放射学报告时，其中80416份报告被归类为坠落阳性。在这些个案中，11 484人与跌倒相关的外因代码(E-code)有关，68 932人与之无关，对应29 280名可能与跌倒有关而未能使用E代码发现的伤者。与嵌入式特征选择方法相比，基于MI的特征选择方法允许我们选择用于分类的判别特征的数目，而嵌入式特征选择方法是自动选择特征数目的。结论机器学习是识别跌倒患者的有效方法。该分类器的开发补充了临床研究人员的工具包，并减少了对编码不足的结构化电子健康记录数据的依赖。
Methods We used the Veterans Aging Cohort Study Virtual Cohort (VACS-VC), an electronic health record-based cohort of 146 530 veterans for whom radiology reports were available (N =2 977 739). We created a reference standard of radiology reports, represented each report by a feature set of words and Unified Medical Language System concepts, and then developed several support vector machine (SVM) classifiers for falls. We compared mutual information (MI) ranking and embedded feature selection approaches. The SVM classifier with MI feature selection was chosen to classify all radiology reports in VACS-VC.Results Our SVM classifier with MI feature selection achieved an area under the curve score of 97.04 on the test set. When applied to all the radiology reports in VACS-VC, 80 416 of these reports were classified as positive for a fall. Of these, 11 484 were associated with a fall-related external cause of injury code (E-code) and 68 932 were not, corresponding to 29 280 patients with potential fall-related injuries who could not have been found using E-codes.Discussion Feature selection was crucial to improving the classifier's performance. Feature selection with MI allowed us to select the number of discriminative features to use for classification, in contrast to the embedded feature selection method, in which the number of features is chosen automatically.Conclusion Machine learning is an effective method of identifying patients who have suffered a fall. The development of this classifier supplements the clinical researcher's toolkit and reduces dependence on under-coded structured electronic health record data.