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首页> 外文期刊>JMIR Medical Informatics >An Attention Model With Transfer Embeddings to Classify Pneumonia-Related Bilingual Imaging Reports: Algorithm Development and Validation
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An Attention Model With Transfer Embeddings to Classify Pneumonia-Related Bilingual Imaging Reports: Algorithm Development and Validation

机译：带传递嵌入的注意力模型对肺炎相关的双语成像报告进行分类：算法开发和验证

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Background In the analysis of electronic health records, proper labeling of outcomes is mandatory. To obtain proper information from radiologic reports, several studies were conducted to classify radiologic reports using deep learning. However, the classification of pneumonia in bilingual radiologic reports has not been conducted previously. Objective The aim of this research was to classify radiologic reports into pneumonia or no pneumonia using a deep learning method. Methods A data set of radiology reports for chest computed tomography and chest x-rays of surgical patients from January 2008 to January 2018 in the Asan Medical Center in Korea was retrospectively analyzed. The classification performance of our long short-term memory (LSTM)–Attention model was compared with various deep learning and machine learning methods. The area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve, sensitivity, specificity, accuracy, and F1 score for the models were compared. Results A total of 5450 radiologic reports were included that contained at least one pneumonia-related word. In the test set (n=1090), our proposed model showed 91.01% (992/1090) accuracy (AUROCs for negative, positive, and obscure were 0.98, 0.97, and 0.90, respectively). The top 3 performances of the models were based on FastText or LSTM. The convolutional neural network–based model showed a lower accuracy 73.03% (796/1090) than the other 2 algorithms. The classification of negative results had an F1 score of 0.96, whereas the classification of positive and uncertain results showed a lower performance (positive F1 score 0.83; uncertain F1 score 0.62). In the extra-validation set, our model showed 80.0% (642/803) accuracy (AUROCs for negative, positive, and obscure were 0.92, 0.96, and 0.84, respectively). Conclusions Our method showed excellent performance in classifying pneumonia in bilingual radiologic reports. The method could enrich the research on pneumonia by obtaining exact outcomes from electronic health data.
机译：背景技术在电子健康记录的分析中，必须适当的结果标记是强制​​性的。为了从放射学报告获得适当的信息，进行了几项研究以使用深度学习对放射学报告进行分类。然而，先前尚未进行双语放射学报告中肺炎的分类。目的本研究的目的是使用深入学习方法将放射性报告分类为肺炎或肺炎。方法回顾性分析了2008年1月至2018年1月至2018年1月的胸部计算机断层扫描和胸X射线的数据集。与各种深度学习和机器学习方法进行了比较了我们长期短期记忆（LSTM）的分类性能。在接收器下的区域下的区域，在精密召回曲线下，面积在精度召回曲线，灵敏度，特异性，精度和F1分数的模型中进行了比较。结果总共包括5450个放射学报告，其中包含至少一种与肺炎相关的词。在测试集（n = 1090）中，我们提出的模型显示出91.01％（992/1090）的精度（阴性，阳性，模糊，分别为0.98,0.97和0.90）。模型的前3个表现基于FastText或LSTM。基于卷积神经网络的模型显示比其他2算法更低73.03％（796/1090）。阴性结果的分类具有0.96的F1得分，而阳性和不确定结果的分类表现出较低的性能（阳性F1得分0.83;不确定F1得分0.62）。在额外验证集中，我们的型号显示了80.0％（642/803）的精度（用于分别为0.92,0.96和0.84的Aurocs分别为0.92,0.96和0.84）。结论我们的方法在双语放射学报告中对肺炎进行了良好的表现。该方法可以通过从电子健康数据获得确切的结果来丰富对肺炎的研究。

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