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

Computer-Aided Diagnosis (230) Clinical Information and Decision Making (1838) Artificial Intelligence (2025) Natural Language Processing (879) Cardiovascular Disease Prevention (196)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/63020, first published .
Autonomous International Classification of Diseases Coding Using Pretrained Language Models and Advanced Prompt Learning Techniques: Evaluation of an Automated Analysis System Using Medical Text

Autonomous International Classification of Diseases Coding Using Pretrained Language Models and Advanced Prompt Learning Techniques: Evaluation of an Automated Analysis System Using Medical Text

Autonomous International Classification of Diseases Coding Using Pretrained Language Models and Advanced Prompt Learning Techniques: Evaluation of an Automated Analysis System Using Medical Text

Authors of this article:

Yan Zhuang1 Author Orcid Image ;   Junyan Zhang1 Author Orcid Image ;   Xiuxing Li2 Author Orcid Image ;   Chao Liu3 Author Orcid Image ;   Yue Yu3 Author Orcid Image ;   Wei Dong4 Author Orcid Image ;   Kunlun He1 Author Orcid Image
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Journals

  1. Lee H, Hwang Y, Kim Y, Park Y, Jo H. Experience of Cardiovascular and Cerebrovascular Disease Surgery Patients: Sentiment Analysis Using the Korean Bidirectional Encoder Representations from Transformers (KoBERT) Model. JMIR Medical Informatics 2025;13:e65127 View
  2. Chomutare T, Svenning T, Hernández M, Ngo P, Budrionis A, Markljung K, Hind L, Torsvik T, Mikalsen K, Babic A, Dalianis H. Artificial Intelligence to Improve Clinical Coding Practice in Scandinavia: Crossover Randomized Controlled Trial. Journal of Medical Internet Research 2025;27:e71904 View