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首页> 外文期刊>BMC Medical Informatics and Decision Making >Automatic detection of actionable radiology reports using bidirectional encoder representations from transformers
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Automatic detection of actionable radiology reports using bidirectional encoder representations from transformers

机译：使用来自变压器的双向编码器表示的自动检测可操作放射学报告

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It is essential for radiologists to communicate actionable findings to the referring clinicians reliably. Natural language processing (NLP) has been shown to help identify free-text radiology reports including actionable findings. However, the application of recent deep learning techniques to radiology reports, which can improve the detection performance, has not been thoroughly examined. Moreover, free-text that clinicians input in the ordering form (order information) has seldom been used to identify actionable reports. This study aims to evaluate the benefits of two new approaches: (1) bidirectional encoder representations from transformers (BERT), a recent deep learning architecture in NLP, and (2) using order information in addition to radiology reports. We performed a binary classification to distinguish actionable reports (i.e., radiology reports tagged as actionable in actual radiological practice) from non-actionable ones (those without an actionable tag). 90,923 Japanese radiology reports in our?hospital were used, of which 788 (0.87%) were actionable. We evaluated four methods, statistical machine learning with logistic regression (LR) and with gradient boosting decision tree (GBDT), and deep learning with a bidirectional long short-term memory (LSTM) model and a publicly available Japanese BERT model. Each method was used with two different inputs, radiology reports alone and pairs of order information and radiology reports. Thus, eight experiments were conducted to examine the performance. Without order information, BERT achieved the highest area under the precision-recall curve (AUPRC) of 0.5138, which showed a statistically significant improvement over LR, GBDT, and LSTM, and the highest area under the receiver operating characteristic curve (AUROC) of 0.9516. Simply coupling the order information with the radiology reports slightly increased the AUPRC of BERT but did not lead to a statistically significant improvement. This may be due to the complexity of clinical decisions made by radiologists. BERT was assumed to be useful to detect actionable reports. More sophisticated methods are required to use order information effectively.
机译：放射科学家至关重要，可可靠地向参考临床医生传达可操作的结果。已显示自然语言处理（NLP）帮助识别自由文本放射学报告，包括可操作的发现。然而，近期应用近期深入学习技术的放射学报告，可以改善检测性能，尚未彻底检查。此外，临床医生在订购表中输入的免费文本（订单信息）很少被用来识别可行的报告。本研究旨在评估两种新方法的好处：（1）来自变压器（BERT）的双向编码器表示，近期NLP中的深度学习架构，（2）除了放射学报告之外还使用订单信息。我们执行了二进制分类，以区分可操作的报告（即，在实际放射实践中标记为可操作的放射学报告）来自不可动作的报告（在实际放射实践中标记为可操作的）（没有可操作标签的那些）。我们使用90,923名日本放射学报告，其中使用了788（0.87％）是可行的。我们评估了四种方法，用Logistic回归（LR）和梯度提升决策树（GBDT）和深度学习，以及双向长期内存（LSTM）模型以及公开的日本BERT模型的深度学习。每种方法都与两种不同的输入一起使用，单独的放射学报告和订单信息和放射学报告。因此，进行了八个实验以检查性能。如果没有订单信息，BERT达到了0.5138的精密召回曲线（AUPRC）下的最高面积，其在LR，GBDT和LSTM上显示出统计学上显着的改善，以及在0.9516的接收器操作特性曲线（AUROC）下的最高面积。 。简单地将订单信息与放射学报告略微增加，伯特的AUPRC略微增加，但没有导致统计上显着的改进。这可能是由于放射科医师制造的临床决策的复杂性。伯特被认为是可用于检测可操作报告的有用。需要有效地使用订单信息需要更复杂的方法。

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