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

Abstract

Background 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. Methods 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. Results 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. Conclusions BERT was assumed to be useful to detect actionable reports. More sophisticated methods are required to use order information effectively.

Impact of a Clinical Text–Based Fall Prediction Model on Preventing Extended Hospital Stays for Elderly Inpatients: Model Development and Performance Evaluation

“…It has often been reported that BERT exhibits high performance, even with clinical text [36][37][38][39]. This is also true for this study, in which a model combining BERT and Bi-LSTM using clinical text recorded in daily practice allowed for fall prediction with an accuracy equal to or higher than that of conventional risk assessment tools.…”

Automatic text classification of actionable radiology reports of tinnitus patients using bidirectional encoder representations from transformer (BERT) and in-domain pre-training (IDPT)

“…Radiology reports are an essential component of big medical data. Previous studies have fully demonstrated the feasibility of extracting evidence from radiology reports to assist clinical diagnosis and prognosis and promote automatic communication between physicians, radiologists and patients[ 37 , 39 , 42 ].However, the full potential of NLP remains to be further discovered, whereas deep learning-based algorithms have nearly revolutionized the paradigm of medical imaging. Radiology reports are primarily intended to provide information to assist with diagnosis; this information must be interpreted by physicians before being transmitted to patients.…”

“…For instance, Zhang et al [ 41 ] designed and evaluated the feasibility of using pre-training models to extract key information from Chinese radiology reports fort lung cancer staging, the model achieved an F1 of 85.96%,while our study achieved an F1 of 84.10%. More recently, Nakamura et al [ 39 ] applied BERT without IDPT to classify actionable Japanese radiology reports, and attempted to predict a positive/negative “actionable tag”, the results seem promising with highest AUC of 0.95. In comparison with previous studies on radiology report classification, the labeling methods applied in this study were more complex, which require both physicians’ clinical experience and priori anatomic knowledge of radiology.…”

“…Traditional symbolic and statistical NLP methods may perform well on questions that can be defined exactly by a certain set of rules or decomposed simply with statistical patterns of terms in a document; both of them have good results in research cases, including data mining of radiology reports [13,14]. Deep learning methods with modified neural networks achieved state-of-the-art results with simplicity, flexibility, and task specificity on large-scale complex tasks [15][16][17]. The convolutional neural network (CNN) and recurrent neural network (RNN) framework has been widely used in classification tasks due to their distinguishing performance in representation learning.…”

Radiology’s “Smart New Deal”

“…While maintaining trust, DxR needs to leverage smartphone capabilities a la Apple or even Google to learn about how, when, where, and who uses report information [ 17 ]. It must learn what information is really understood, what is actually used for decisions and actions, and, in particular, what information is not used [ 18 , 19 ]. Unused information has negative value as it unnecessarily wastes time.…”