A novel deep learning approach to extract Chinese clinical entities for lung cancer screening and staging

• Published in: BMC medical informatics and decision making Vol. 21; no. Suppl 2; pp. 1 - 214
• Main Authors: Zhang, Huanyao, Hu, Danqing, Duan, Huilong, Li, Shaolei, Wu, Nan, Lu, Xudong
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 30.07.2021; BioMed Central; BMC
• Subjects: Algorithms; Annotations; BERT; Cancer; Cancer screening; Computed tomography; CT reports; Decision making; Deep learning; Diagnosis; Electronic health records; Embedding; Evaluation; Health informatics; Long short-term memory; Lung cancer; Lung cancer screening and staging; Machine learning; Medical screening; Named entity recognition; Neural networks; Pre-training; Radiology; Semantics; Thoracic surgery; Thorax; Training; Transformer; Transformers; Tumor staging; China
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-021-01575-x

Abstract Background Computed tomography (CT) reports record a large volume of valuable information about patients’ conditions and the interpretations of radiology images from radiologists, which can be used for clinical decision-making and further academic study. However, the free-text nature of clinical reports is a critical barrier to use this data more effectively. In this study, we investigate a novel deep learning method to extract entities from Chinese CT reports for lung cancer screening and TNM staging. Methods The proposed approach presents a new named entity recognition algorithm, namely the BERT-based-BiLSTM-Transformer network (BERT-BTN) with pre-training, to extract clinical entities for lung cancer screening and staging. Specifically, instead of traditional word embedding methods, BERT is applied to learn the deep semantic representations of characters. Following the long short-term memory layer, a Transformer layer is added to capture the global dependencies between characters. Besides, pre-training technique is employed to alleviate the problem of insufficient labeled data. Results We verify the effectiveness of the proposed approach on a clinical dataset containing 359 CT reports collected from the Department of Thoracic Surgery II of Peking University Cancer Hospital. The experimental results show that the proposed approach achieves an 85.96% macro-F1 score under exact match scheme, which improves the performance by 1.38%, 1.84%, 3.81%,4.29%,5.12%,5.29% and 8.84% compared to BERT-BTN, BERT-LSTM, BERT-fine-tune, BERT-Transformer, FastText-BTN, FastText-BiLSTM and FastText-Transformer, respectively. Conclusions In this study, we developed a novel deep learning method, i.e., BERT-BTN with pre-training, to extract the clinical entities from Chinese CT reports. The experimental results indicate that the proposed approach can efficiently recognize various clinical entities about lung cancer screening and staging, which shows the potential for further clinical decision-making and academic research.