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Li J, Lin Y, Zhao P, Liu W, Cai L, Sun J, Zhao L, Yang Z, Song H, Lv H, Wang Z. Automatic text classification of actionable radiology reports of tinnitus patients using bidirectional encoder representations from transformer (BERT) and in-domain pre-training (IDPT). BMC Med Inform Decis Mak 2022; 22:200. [PMID: 35907966 PMCID: PMC9338483 DOI: 10.1186/s12911-022-01946-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 07/18/2022] [Indexed: 11/17/2022] Open
Abstract
Background Given the increasing number of people suffering from tinnitus, the accurate categorization of patients with actionable reports is attractive in assisting clinical decision making. However, this process requires experienced physicians and significant human labor. Natural language processing (NLP) has shown great potential in big data analytics of medical texts; yet, its application to domain-specific analysis of radiology reports is limited. Objective The aim of this study is to propose a novel approach in classifying actionable radiology reports of tinnitus patients using bidirectional encoder representations from transformer BERT-based models and evaluate the benefits of in domain pre-training (IDPT) along with a sequence adaptation strategy. Methods A total of 5864 temporal bone computed tomography(CT) reports are labeled by two experienced radiologists as follows: (1) normal findings without notable lesions; (2) notable lesions but uncorrelated to tinnitus; and (3) at least one lesion considered as potential cause of tinnitus. We then constructed a framework consisting of deep learning (DL) neural networks and self-supervised BERT models. A tinnitus domain-specific corpus is used to pre-train the BERT model to further improve its embedding weights. In addition, we conducted an experiment to evaluate multiple groups of max sequence length settings in BERT to reduce the excessive quantity of calculations. After a comprehensive comparison of all metrics, we determined the most promising approach through the performance comparison of F1-scores and AUC values. Results In the first experiment, the BERT finetune model achieved a more promising result (AUC-0.868, F1-0.760) compared with that of the Word2Vec-based models(AUC-0.767, F1-0.733) on validation data. In the second experiment, the BERT in-domain pre-training model (AUC-0.948, F1-0.841) performed significantly better than the BERT based model(AUC-0.868, F1-0.760). Additionally, in the variants of BERT fine-tuning models, Mengzi achieved the highest AUC of 0.878 (F1-0.764). Finally, we found that the BERT max-sequence-length of 128 tokens achieved an AUC of 0.866 (F1-0.736), which is almost equal to the BERT max-sequence-length of 512 tokens (AUC-0.868,F1-0.760). Conclusion In conclusion, we developed a reliable BERT-based framework for tinnitus diagnosis from Chinese radiology reports, along with a sequence adaptation strategy to reduce computational resources while maintaining accuracy. The findings could provide a reference for NLP development in Chinese radiology reports. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-022-01946-y.
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Affiliation(s)
- Jia Li
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing, 100050, People's Republic of China
| | - Yucong Lin
- School of Medical Technology, Beijing Institute of Technology, No.5 Zhongguancun East Road, Beijing, 100050, People's Republic of China
| | - Pengfei Zhao
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing, 100050, People's Republic of China
| | - Wenjuan Liu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing, 100050, People's Republic of China
| | - Linkun Cai
- School of Biological Science and Medical Engineering, Beihang University, No.37 XueYuan Road, Beijing, 100191, People's Republic of China
| | - Jing Sun
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing, 100050, People's Republic of China
| | - Lei Zhao
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing, 100050, People's Republic of China
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing, 100050, People's Republic of China
| | - Hong Song
- School of Computer Science and Technology, Beijing Institute of Technology, No. 5, South Street, Zhongguancun, Haidian District, Beijing, 100050, People's Republic of China.
| | - Han Lv
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing, 100050, People's Republic of China.
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing, 100050, People's Republic of China. .,School of Biological Science and Medical Engineering, Beihang University, No.37 XueYuan Road, Beijing, 100191, People's Republic of China.
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Nada A, Agunbiade SA, Whitehead MT, Cousins JP, Ahsan H, Mahdi E. Cross-Sectional Imaging Evaluation of Congenital Temporal Bone Anomalies: What Each Radiologist Should Know. Curr Probl Diagn Radiol 2020; 50:716-724. [PMID: 32951949 DOI: 10.1067/j.cpradiol.2020.08.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 08/09/2020] [Accepted: 08/21/2020] [Indexed: 11/22/2022]
Abstract
Hearing loss in pediatric age group is associated with many congenital temporal bone disorders. Aberrant development of various ear structures leads into either conductive or sensorineural hearing loss. Knowledge of the embryology and anatomical details of various compartments of the ear help better understanding of such disorders. In general, abnormalities of external and middle ears result in conductive hearing loss. Whereas abnormalities of inner ear structures lead into sensorineural hearing loss. These abnormalities could occur as isolated or part of syndromes. Temporal bone disorders are a significant cause of morbidity and developmental delays in children. Imaging evaluation of children presented with hearing loss is paramount in early diagnosis and proper management planning. Our aim is to briefly discuss embryology and anatomy of the pediatric petrous temporal bones. The characteristic imaging features of commonly encountered congenital temporal bone disorders and their associated syndromes will be discussed.
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Affiliation(s)
- A Nada
- Diagnostic Radiology Resident, Department of Radiology, University of Missouri Health care. One Hospital Drive, Columbia, MO.
| | - S A Agunbiade
- Diagnostic Radiology Resident, Department of Radiology, University of Missouri Health care. One Hospital Drive, Columbia, MO
| | - M T Whitehead
- Department of Diagnostic Imaging and Radiology, Children's National Medical Center, Washington, DC; George Washington University Hospital, Washington, DC
| | - J P Cousins
- Diagnostic Radiology Resident, Department of Radiology, University of Missouri Health care. One Hospital Drive, Columbia, MO
| | - H Ahsan
- Diagnostic Radiology Resident, Department of Radiology, University of Missouri Health care. One Hospital Drive, Columbia, MO
| | - E Mahdi
- Diagnostic Radiology Resident, Department of Radiology, University of Missouri Health care. One Hospital Drive, Columbia, MO
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