使用临床叙述和结构化数据识别乳腺癌的远处复发
Using Clinical Narratives and Structured Data to Identify Distant Recurrences in Breast Cancer
摘要：
从电子健康记录（EHR）中准确识别乳腺癌的远处复发对于临床护理和二次分析都很重要。尽管已经开发了多种应用程序用于乳腺癌的计算表型，但远处复发识别仍然严重依赖于手动图表审查。在这项研究中，我们的目标是开发一个模型，识别远处复发的乳腺癌使用临床叙述和结构化数据从电子病历。我们应用MetaMap从临床叙述中提取特征，并从EHR中检索结构化的临床数据。利用这些特征，我们训练一个支持向量机模型来识别乳腺癌患者的远处复发。我们使用1,396个双注释的受试者对模型进行训练，并使用599个双注释的受试者对模型进行验证。此外，我们在一组4，904个单注释主题上验证了该模型作为泛化测试。我们在使用训练数据集的交叉验证中获得了0.92（SD=0.01）的高曲线下面积（AUC）得分，然后在分别使用599和4，904个样本的保持测试和泛化测试中获得了0.95和0.93的AUC得分。我们的模型可以通过结合从非结构化临床叙述和结构化临床数据中提取的特征来准确有效地识别乳腺癌的远处复发。
英文摘要：
Accurately identifying distant recurrences in breast cancer from the Electronic Health Records (EHR) is important for both clinical care and secondary analysis. Although multiple applications have been developed for computational phenotyping in breast cancer, distant recurrence identification still relies heavily on manual chart review. In this study, we aim to develop a model that identifies distant recurrences in breast cancer using clinical narratives and structured data from EHR. We apply MetaMap to extract features from clinical narratives and also retrieve structured clinical data from EHR. Using these features, we train a support vector machine model to identify distant recurrences in breast cancer patients. We train the model using 1,396 double-annotated subjects and validate the model using 599 double-annotated subjects. In addition, we validate the model on a set of 4,904 single-annotated subjects as a generalization test. We obtained a high area under curve (AUC) score of 0.92 (SD=0.01) in the cross-validation using the training dataset, then obtained AUC scores of 0.95 and 0.93 in the held-out test and generalization test using 599 and 4,904 samples respectively. Our model can accurately and efficiently identify distant recurrences in breast cancer by combining features extracted from unstructured clinical narratives and structured clinical data.
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