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

Machine Learning (1465) Clinical Information and Decision Making (1407) Ontologies, Classifications, and Coding (239) Natural Language Processing (693) Electronic Health Records (1053)

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 Chen1, 2 Author Orcid Image ;   Ssu-Ming Wang1 Author Orcid Image ;   Wei-Chih Liao1 Author Orcid Image ;   Lu-Cheng Kuo3 Author Orcid Image ;   Kuan-Chih Chen1, 4 Author Orcid Image ;   Yu-Cheng Lin5, 6 Author Orcid Image ;   Chi-Yu Yang7, 8 Author Orcid Image ;   Chi-Hao Chiu9 Author Orcid Image ;   Shu-Chih Chang10 Author Orcid Image ;   Feipei Lai1, 11, 12 Author Orcid Image
Article Authors Cited by (34) 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
  9. Zhang Y, Li X, Liu Y, Li A, Yang X, Tang X. A Multilabel Text Classifier of Cancer Literature at the Publication Level: Methods Study of Medical Text Classification. JMIR Medical Informatics 2023;11:e44892 View
  10. Masud J, Kuo C, Yeh C, Yang H, Lin M. Applying Deep Learning Model to Predict Diagnosis Code of Medical Records. Diagnostics 2023;13(13):2297 View
  11. Scendoni R, Tomassini L, Cingolani M, Perali A, Pilati S, Fedeli P. Artificial Intelligence in Evaluation of Permanent Impairment: New Operational Frontiers. Healthcare 2023;11(14):1979 View
  12. Gabriel R, Mariano E, McAuley J, Wu C. How large language models can augment perioperative medicine: a daring discourse. Regional Anesthesia & Pain Medicine 2023;48(11):575 View
  13. van Leeuwen J, Penne E, Rabelink T, Knevel R, Teng Y. Using an artificial intelligence tool incorporating natural language processing to identify patients with a diagnosis of ANCA-associated vasculitis in electronic health records. Computers in Biology and Medicine 2024;168:107757 View
  14. Miao J, Thongprayoon C, Suppadungsuk S, Garcia Valencia O, Qureshi F, Cheungpasitporn W. Innovating Personalized Nephrology Care: Exploring the Potential Utilization of ChatGPT. Journal of Personalized Medicine 2023;13(12):1681 View
  15. Yu J, Petersen C, Reid S, Rosenbloom S, Warner J. Telehealth and Technology. The Cancer Journal 2024;30(1):40 View
  16. Essay P, Rajasekharan A. Robust diagnosis recommendation system for Primary Care Telemedicine using long short-term memory multi-class sequence classification. Heliyon 2024;10(6):e26770 View
  17. Marcou Q, Berti-Equille L, Novelli N. Creating a computer assisted ICD coding system: Performance metric choice and use of the ICD hierarchy. Journal of Biomedical Informatics 2024;152:104617 View
  18. Falter M, Godderis D, Scherrenberg M, Kizilkilic S, Xu L, Mertens M, Jansen J, Legroux P, Kindermans H, Sinnaeve P, Neven F, Dendale P. Using natural language processing for automated classification of disease and to identify misclassified ICD codes in cardiac disease. European Heart Journal - Digital Health 2024;5(3):229 View
  19. Paleczny S, Osagie N, Sethi J, Simmen H. Validity and reliability International Classification of Diseases-10 codes for all forms of injury: A systematic review. PLOS ONE 2024;19(2):e0298411 View
  20. Chomutare T, Lamproudis A, Budrionis A, Svenning T, Hind L, Ngo P, Mikalsen K, Dalianis H. Improving Quality of ICD-10 (International Statistical Classification of Diseases, Tenth Revision) Coding Using AI: Protocol for a Crossover Randomized Controlled Trial. JMIR Research Protocols 2024;13:e54593 View
  21. Silva H, Duque V, Macedo M, Mendes M. Aiding ICD-10 Encoding of Clinical Health Records Using Improved Text Cosine Similarity and PLM-ICD. Algorithms 2024;17(4):144 View
  22. Ji S, Li X, Sun W, Dong H, Taalas A, Zhang Y, Wu H, Pitkänen E, Marttinen P. A Unified Review of Deep Learning for Automated Medical Coding. ACM Computing Surveys 2024;56(12):1 View
  23. AYDOGAN-KILIC D, KILIC D, NIELSEN I. EXAMINATION OF SUMMARIZED MEDICAL RECORDS FOR ICD CODE CLASSIFICATION VIA BERT. Applied Computer Science 2024;20(2):60 View
  24. Móstoles R, Araque O, Iglesias C. Using Enhanced Representations to Predict Medical Procedures from Clinician Notes. Applied Sciences 2024;14(15):6431 View
  25. Lee J, Sharma I, Arcaro N, Blackstone E, Gillinov A, Svensson L, Karamlou T, Chen D. Automating surgical procedure extraction for society of surgeons adult cardiac surgery registry using pretrained language models. JAMIA Open 2024;7(3) View
  26. Wang K, Tan X, Nan S, Sang L, Chen H, Duan H. OLR-Net: Object Label Retrieval Network for principal diagnosis extraction. Computers in Biology and Medicine 2024;182:109130 View
  27. Dai H, Wang C, Chen C, Liou C, Lu A, Lai C, Shain B, Ke C, Wang W, Mir T, Simanjuntak M, Kao H, Tsai M, Tseng V. Evaluating a Natural Language Processing–Driven, AI-Assisted International Classification of Diseases, 10th Revision, Clinical Modification, Coding System for Diagnosis Related Groups in a Real Hospital Environment: Algorithm Development and Validation Study. Journal of Medical Internet Research 2024;26:e58278 View
  28. Carberry J, Xu H. GPT-Enhanced Hierarchical Deep Learning Model for Automated ICD Coding. Advances in Science, Technology and Engineering Systems Journal 2024;9(4):21 View
  29. Sitaru S, Nhan F, Gasteiger C, Rueckert D, Biedermann T, Zink A. Digitalising the past decades: automated ICD-10 coding of unstructured free text dermatological diagnoses. BMC Health Services Research 2024;24(1) View

Books/Policy Documents

  1. Hatoum M, Charr J, Ghaddar A, Guyeux C, Laiymani D. Agents and Artificial Intelligence. View
  2. Xu H, Demner Fushman D, Hong N, Raja K. Natural Language Processing in Biomedicine. View