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

Formative Evaluation of Digital Health Interventions (2837) Natural Language Processing (804)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41137, first published .
Fine-tuning Strategies for Classifying Community-Engaged Research Studies Using Transformer-Based Models: Algorithm Development and Improvement Study

Fine-tuning Strategies for Classifying Community-Engaged Research Studies Using Transformer-Based Models: Algorithm Development and Improvement Study

Fine-tuning Strategies for Classifying Community-Engaged Research Studies Using Transformer-Based Models: Algorithm Development and Improvement Study

Authors of this article:

Brian J Ferrell1 Author Orcid Image
Article Authors Cited by (6) Tweetations (2) Metrics

Journals

  1. Ferrell B, Raskin S, Zimmerman E. Calibrating a Transformer-Based Model’s Confidence on Community-Engaged Research Studies: Decision Support Evaluation Study. JMIR Formative Research 2023;7:e41516 View
  2. Yu S, Yang S, Yoon S. The Design of an Intelligent Lightweight Stock Trading System Using Deep Learning Models: Employing Technical Analysis Methods. Systems 2023;11(9):470 View
  3. Baminiwatte R, Torsu B, Scherbakov D, Mollalo A, Obeid J, Alekseyenko A, Lenert L. Machine learning in healthcare citizen science: A scoping review. International Journal of Medical Informatics 2025;195:105766 View
  4. Rajkumardheivanayahi P, Berry R, Costagliola N, Fiondella L, Bastian N, Kul G. Explainability of Network Intrusion Detection Using Transformers: A Packet-Level Approach. IEEE Access 2025;13:5154 View

Books/Policy Documents

  1. Youn J, Kang D, Lim H, Kim M. Human Brain and Artificial Intelligence. View

Conference Proceedings

  1. Pake T, Pachareney U. 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). Fine-Tuning Strategies for Transformers View