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

Machine Learning (1053) Clinical Information and Decision Making (945) Ontologies, Classifications, and Coding (165) Natural Language Processing (507) Electronic Health Records (809)

Published on in Vol 9 , No 8 (2021) :August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23230, first published .
Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning

Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning

Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning

Authors of this article:

Pei-Fu Chen 1, 2 Author Orcid Image ;   Ssu-Ming Wang 1 Author Orcid Image ;   Wei-Chih Liao 1 Author Orcid Image ;   Lu-Cheng Kuo 3 Author Orcid Image ;   Kuan-Chih Chen 1, 4 Author Orcid Image ;   Yu-Cheng Lin 5, 6 Author Orcid Image ;   Chi-Yu Yang 7, 8 Author Orcid Image ;   Chi-Hao Chiu 9 Author Orcid Image ;   Shu-Chih Chang 10 Author Orcid Image ;   Feipei Lai 1, 11, 12 Author Orcid Image
Article Authors Cited by (10) Tweetations (3) Metrics

Journals

  1. Chen P, Chen K, Liao W, Lai F, He T, Lin S, Chen W, Yang C, Lin Y, Tsai I, Chiu C, Chang S, Hung F. Automatic International Classification of Diseases Coding System: Deep Contextualized Language Model With Rule-Based Approaches. JMIR Medical Informatics 2022;10(6):e37557 View
  2. Kesiku C, Chaves-Villota A, Garcia-Zapirain B. Natural Language Processing Techniques for Text Classification of Biomedical Documents: A Systematic Review. Information 2022;13(10):499 View
  3. Arivazhagan N, Van Vleck T. Natural Language Processing Basics. Clinical Journal of the American Society of Nephrology 2023;18(3):400 View
  4. Chen P, He T, Lin S, Chu Y, Kuo C, Lai F, Wang S, Zhu W, Chen K, Kuo L, Hung F, Lin Y, Tsai I, Chiu C, Chang S, Yang C. Training a Deep Contextualized Language Model for International Classification of Diseases, 10th Revision Classification via Federated Learning: Model Development and Validation Study. JMIR Medical Informatics 2022;10(11):e41342 View
  5. Chen J, Guo C, Lu M, Ding S. Unifying Diagnosis Identification and Prediction Method Embedding the Disease Ontology Structure From Electronic Medical Records. Frontiers in Public Health 2022;9 View
  6. Chizhikova M, López-Úbeda P, Collado-Montañez J, Martín-Noguerol T, Díaz-Galiano M, Luna A, Ureña-López L, Martín-Valdivia M. CARES: A Corpus for classification of Spanish Radiological reports. Computers in Biology and Medicine 2023;154:106581 View
  7. Anetta K, Horak A, Wojakowski W, Wita K, Jadczyk T. Deep Learning Analysis of Polish Electronic Health Records for Diagnosis Prediction in Patients with Cardiovascular Diseases. Journal of Personalized Medicine 2022;12(6):869 View
  8. Singaravelan A, Hsieh C, Liao Y, Hsu J. Predicting ICD-9 Codes Using Self-Report of Patients. Applied Sciences 2021;11(21):10046 View