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

Machine Learning (1370) Clinical Informatics (904) Natural Language Processing (636)

Published on in Vol 8, No 4 (2020): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17787, first published .
Modified Bidirectional Encoder Representations From Transformers Extractive Summarization Model for Hospital Information Systems Based on Character-Level Tokens (AlphaBERT): Development and Performance Evaluation

Modified Bidirectional Encoder Representations From Transformers Extractive Summarization Model for Hospital Information Systems Based on Character-Level Tokens (AlphaBERT): Development and Performance Evaluation

Modified Bidirectional Encoder Representations From Transformers Extractive Summarization Model for Hospital Information Systems Based on Character-Level Tokens (AlphaBERT): Development and Performance Evaluation

Authors of this article:

Yen-Pin Chen1, 2, 3 Author Orcid Image ;   Yi-Ying Chen3 Author Orcid Image ;   Jr-Jiun Lin3 Author Orcid Image ;   Chien-Hua Huang3, 4 Author Orcid Image ;   Feipei Lai1, 5, 6 Author Orcid Image
Article Authors Cited by (16) Tweetations (2) Metrics

Journals

  1. Kim Y, Lee J, Choi S, Lee J, Kim J, Seok J, Joo H. Validation of deep learning natural language processing algorithm for keyword extraction from pathology reports in electronic health records. Scientific Reports 2020;10(1) View
  2. Chen Y, Lo Y, Lai F, Huang C. Disease Concept-Embedding Based on the Self-Supervised Method for Medical Information Extraction from Electronic Health Records and Disease Retrieval: Algorithm Development and Validation Study. Journal of Medical Internet Research 2021;23(1):e25113 View
  3. Sorin V, Barash Y, Konen E, Klang E. Deep-learning natural language processing for oncological applications. The Lancet Oncology 2020;21(12):1553 View
  4. Mohammed A, Ali A. Survey of BERT (Bidirectional Encoder Representation Transformer) types. Journal of Physics: Conference Series 2021;1963(1):012173 View
  5. Chen J, Baxter S. Applications of natural language processing in ophthalmology: present and future. Frontiers in Medicine 2022;9 View
  6. Chaves A, Kesiku C, Garcia-Zapirain B. Automatic Text Summarization of Biomedical Text Data: A Systematic Review. Information 2022;13(8):393 View
  7. Kalyan K, Rajasekharan A, Sangeetha S. AMMU: A survey of transformer-based biomedical pretrained language models. Journal of Biomedical Informatics 2022;126:103982 View
  8. Cai L, Li J, Lv H, Liu W, Niu H, Wang Z. Integrating domain knowledge for biomedical text analysis into deep learning: A survey. Journal of Biomedical Informatics 2023;143:104418 View
  9. Keszthelyi D, Gaudet-Blavignac C, Bjelogrlic M, Lovis C. Patient Information Summarization in Clinical Settings: Scoping Review. JMIR Medical Informatics 2023;11:e44639 View
  10. Wang Y, Hu S, Yao J, Pan Y, Wang J, Wang H. Clinical nursing mentors’ motivation, attitude, and practice for mentoring and factors associated with them. BMC Nursing 2024;23(1) View
  11. Siebra C, Kurpicz-Briki M, Wac K. Transformers in health: a systematic review on architectures for longitudinal data analysis. Artificial Intelligence Review 2024;57(2) View
  12. Kim S, Shin W, Kim H. Unravelling long-stay tourist experiences and satisfaction: text mining and deep learning approaches. Current Issues in Tourism 2024:1 View
  13. Lyu D, Wang X, Chen Y, Wang F. Language model and its interpretability in biomedicine: A scoping review. iScience 2024;27(4):109334 View
  14. Preiksaitis C, Ashenburg N, Bunney G, Chu A, Kabeer R, Riley F, Ribeira R, Rose C. The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review. JMIR Medical Informatics 2024;12:e53787 View

Books/Policy Documents

  1. Voloch N, Gudes E, Gal-Oz N, Mitrany R, Shani O, Shoel M. Cyber Security, Cryptology, and Machine Learning. View