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

New Methods (1030) Natural Language Processing (801) Clinical Information and Decision Making (1656) Machine Learning (1738) Artificial Intelligence (1740)

Published on in Vol 24, No 12 (2022): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38859, first published .
Predicting Publication of Clinical Trials Using Structured and Unstructured Data: Model Development and Validation Study

Predicting Publication of Clinical Trials Using Structured and Unstructured Data: Model Development and Validation Study

Predicting Publication of Clinical Trials Using Structured and Unstructured Data: Model Development and Validation Study

Authors of this article:

Siyang Wang1 Author Orcid Image ;   Simon Šuster1 Author Orcid Image ;   Timothy Baldwin1, 2 Author Orcid Image ;   Karin Verspoor3 Author Orcid Image
Article Authors Cited by (4) Tweetations (5) Metrics

Journals

  1. Ghim J, Ahn S. Transforming clinical trials: the emerging roles of large language models. Translational and Clinical Pharmacology 2023;31(3):131 View
  2. Šuster S, Baldwin T, Verspoor K. Zero‐ and few‐shot prompting of generative large language models provides weak assessment of risk of bias in clinical trials. Research Synthesis Methods 2024;15(6):988 View
  3. Alkhawaldeh I, Abdelhady M, Aljabali A, Abo-elnour D, Elhalag R, Zedan E, Rizk M, Kabeel M, Alkasasbeh J, Elejla S, Alkasabrah A, Salama M, Ghaith H, Negida A. Rates of discontinuation and non-publication of endovascular stroke clinical studies: A cross-sectional analysis. Journal of Clinical Neuroscience 2025;135:111122 View
  4. Shu H, Zhu Y. The Exploration of a Causal Mechanism for Corporate Environmental Performance in Hydropower Engineering Enterprises: Evidence from China and the United States. Sustainability 2025;17(8):3391 View