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

Clinical Information and Decision Making (1426) Decision Support for Health Professionals (1203) Natural Language Processing (704) Machine Learning (1491) Diabetes (192) Ontologies, Classifications, and Coding (240) Information Retrieval (294)

Published on in Vol 24, No 1 (2022): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27434, first published .
Improving Diabetes-Related Biomedical Literature Exploration in the Clinical Decision-making Process via Interactive Classification and Topic Discovery: Methodology Development Study

Improving Diabetes-Related Biomedical Literature Exploration in the Clinical Decision-making Process via Interactive Classification and Topic Discovery: Methodology Development Study

Improving Diabetes-Related Biomedical Literature Exploration in the Clinical Decision-making Process via Interactive Classification and Topic Discovery: Methodology Development Study

Authors of this article:

Adrian Ahne1, 2 Author Orcid Image ;   Guy Fagherazzi3 Author Orcid Image ;   Xavier Tannier4 Author Orcid Image ;   Thomas Czernichow2 Author Orcid Image ;   Francisco Orchard2 Author Orcid Image
Article Authors Cited by (5) Tweetations (16) Metrics

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  2. Huang G, Li Y, Jameel S, Long Y, Papanastasiou G. From explainable to interpretable deep learning for natural language processing in healthcare: How far from reality?. Computational and Structural Biotechnology Journal 2024;24:362 View
  3. Arslantaş S. Artificial intelligence and big data from digital health applications: publication trends and analysis. Journal of Health Organization and Management 2024 View