Automatic detection of actionable radiology reports using bidirectional encoder representations from transformers

BMC Medical Informatics and Decision Making

Yuta Nakamura¹  Osamu Abe¹  Takeyuki Watadani¹  Shouhei Hanaoka¹  Takeharu Yoshikawa²  Soichiro Miki²  Takahiro Nakao²  Yukihiro Nomura²  Naoto Hayashi² 

[1] Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, 113-8655, Tokyo, Japan;The Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, 113-8655, Tokyo, Japan;The Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, 113-8655, Tokyo, Japan;

关键词: Radiology reports;    Actionable finding;    Natural language processing (NLP);    Bidirectional encoder representations from transformers (BERT);    Deep learning;   

DOI  :  10.1186/s12911-021-01623-6

来源: Springer

【 摘 要 】

BackgroundIt 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.MethodsWe 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.ResultsWithout 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.ConclusionsBERT was assumed to be useful to detect actionable reports. More sophisticated methods are required to use order information effectively.

【 授权许可】

CC BY