This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (1320) Decision Support for Health Professionals (1133) Machine Learning (1378) Natural Language Processing (638) Innovations and Technology in Cancer Care (400)

Published on in Vol 23, No 1 (2021): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19689, first published .
Use of BERT (Bidirectional Encoder Representations from Transformers)-Based Deep Learning Method for Extracting Evidences in Chinese Radiology Reports: Development of a Computer-Aided Liver Cancer Diagnosis Framework

Use of BERT (Bidirectional Encoder Representations from Transformers)-Based Deep Learning Method for Extracting Evidences in Chinese Radiology Reports: Development of a Computer-Aided Liver Cancer Diagnosis Framework

Use of BERT (Bidirectional Encoder Representations from Transformers)-Based Deep Learning Method for Extracting Evidences in Chinese Radiology Reports: Development of a Computer-Aided Liver Cancer Diagnosis Framework

Authors of this article:

Honglei Liu1, 2 Author Orcid Image ;   Zhiqiang Zhang1, 2 Author Orcid Image ;   Yan Xu3 Author Orcid Image ;   Ni Wang1, 2 Author Orcid Image ;   Yanqun Huang1, 2 Author Orcid Image ;   Zhenghan Yang3 Author Orcid Image ;   Rui Jiang4 Author Orcid Image ;   Hui Chen1, 2 Author Orcid Image
Article Authors Cited by (35) Tweetations (6) Metrics

Journals

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  10. Liu W, Zhang X, Lv H, Li J, Liu Y, Yang Z, Weng X, Lin Y, Song H, Wang Z. Using a classification model for determining the value of liver radiological reports of patients with colorectal cancer. Frontiers in Oncology 2022;12 View
  11. Kuling G, Curpen B, Martel A. BI-RADS BERT and Using Section Segmentation to Understand Radiology Reports. Journal of Imaging 2022;8(5):131 View
  12. Binsfeld Gonçalves L, Nesic I, Obradovic M, Stieltjes B, Weikert T, Bremerich J. Natural Language Processing and Graph Theory: Making Sense of Imaging Records in a Novel Representation Frame. JMIR Medical Informatics 2022;10(12):e40534 View
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  15. Olthof A, van Ooijen P, Cornelissen L. Deep Learning-Based Natural Language Processing in Radiology: The Impact of Report Complexity, Disease Prevalence, Dataset Size, and Algorithm Type on Model Performance. Journal of Medical Systems 2021;45(10) View
  16. 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 Medical Informatics and Decision Making 2022;22(1) View
  17. Park W, Siddiqui I, Chakraborty C, Qureshi N, Shin D. Scarcity-aware spam detection technique for big data ecosystem. Pattern Recognition Letters 2022;157:67 View
  18. Wan C, Feng W, Ma R, Ma H, Wang J, Huang R, Zhang X, Jing M, Yang H, Yu H, Liu Y. Association between depressive symptoms and diagnosis of diabetes and its complications: A network analysis in electronic health records. Frontiers in Psychiatry 2022;13 View
  19. Abbasi N, Lacson R, Kapoor N, Licaros A, Guenette J, Burk K, Hammer M, Desai S, Eappen S, Saini S, Khorasani R. Development and External Validation of an Artificial Intelligence Model for Identifying Radiology Reports Containing Recommendations for Additional Imaging. American Journal of Roentgenology 2023;221(3):377 View
  20. Yang L, Huang X, Wang J, Yang X, Ding L, Li Z, Li J. Identifying stroke-related quantified evidence from electronic health records in real-world studies. Artificial Intelligence in Medicine 2023;140:102552 View
  21. Cai J, Chen S, Guo S, Wang S, Li L, Liu X, Zheng K, Liu Y, Chen S. RegEMR: a natural language processing system to automatically identify premature ovarian decline from Chinese electronic medical records. BMC Medical Informatics and Decision Making 2023;23(1) View
  22. Balamurugan R, Mohite S, Raja S. Protein Sequence Classification Using Bidirectional Encoder Representations from Transformers (BERT) Approach. SN Computer Science 2023;4(5) View
  23. Lee K, Liu Z, Chandran U, Kalsekar I, Laxmanan B, Higashi M, Jun T, Ma M, Li M, Mai Y, Gilman C, Wang T, Ai L, Aggarwal P, Pan Q, Oh W, Stolovitzky G, Schadt E, Wang X. Detecting Ground Glass Opacity Features in Patients With Lung Cancer: Automated Extraction and Longitudinal Analysis via Deep Learning–Based Natural Language Processing. JMIR AI 2023;2:e44537 View
  24. Wang Y, Zhang W, Yang Y, Sun J, Wang L. Survival Prediction of Esophageal Squamous Cell Carcinoma Based on the Prognostic Index and Sparrow Search Algorithm-Support Vector Machine. Current Bioinformatics 2023;18(7):598 View
  25. Reisinezhad P, Fakhrahmad M. Induction of knowledge, attitude and practice of people toward a pandemic from Twitter: a comprehensive model based on opinion mining. Kybernetes 2023;52(7):2507 View
  26. Qiu Y, Fang J. Application of Knowledge Sharing Decision Model Based on Computer-Aided System in Student Education Management Platform. International Journal of Knowledge Management 2024;20(1):1 View
  27. Gorenstein L, Konen E, Green M, Klang E. Bidirectional Encoder Representations from Transformers in Radiology: A Systematic Review of Natural Language Processing Applications. Journal of the American College of Radiology 2024;21(6):914 View
  28. Wang L, Ma Y, Bi W, Lv H, Li Y. An Entity Extraction Pipeline for Medical Text Records Using Large Language Models: Analytical Study. Journal of Medical Internet Research 2024;26:e54580 View
  29. Wu Z, Xu D, Hu P, Li L, Huang T. A meta-path, attention-based deep learning method to support hepatitis carcinoma predictions for improved cirrhosis patient management. Decision Support Systems 2024;181:114226 View
  30. Gu K, Lee J, Shin J, Hwang J, Min J, Jeong W, Lee M, Song K, Bae S. Using GPT‐4 for LI‐RADS feature extraction and categorization with multilingual free‐text reports. Liver International 2024;44(7):1578 View

Books/Policy Documents

  1. Karaca Y, Zhang Y, Dursun A, Wang S. Multi-Chaos, Fractal and Multi-Fractional Artificial Intelligence of Different Complex Systems. View
  2. González Esparza H, Florencia R, Díaz Román J, Mendoza-Carreón A. Innovations in Machine and Deep Learning. View
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