Overview
The CT-DEB’26 Shared Task is an open machine learning challenge focused on predicting medication dosing errors in clinical trials. This initiative is part of the CL4Health’26 Workshop at LREC 2026, aiming to enhance the safety and efficiency of clinical trial protocols. Participants are encouraged to submit papers detailing their methodologies, contributing to the collective knowledge in this critical area of healthcare.
Background & Relevance
Medication errors, particularly those related to dosing, pose significant challenges in clinical trials, leading to delays, increased costs, and potential patient harm. The CT-DEB’26 initiative seeks to develop robust and transparent machine learning methods that can proactively identify clinical trial protocols at risk of such errors. By addressing this issue, the task aims to improve patient safety and streamline clinical trial processes, making it a vital endeavor for the AI and healthcare communities.
Key Details
- Event: CT-DEB’26 Shared Task
- Workshop: CL4Health’26 at LREC 2026
- Dataset: ct-dosing-errors-benchmark (available on HuggingFace)
- Primary Metric: ROC-AUC
- Submission Platform: CodaBench
Timeline
- Phase 1:
- December 1, 2025: Release of training and validation features
- January 10, 2026: Phase 1 submission deadline
- January 12, 2026: Validation leaderboard revealed
- Phase 2:
- January 13, 2026: Test features release
- January 27, 2026: Phase 2 submission deadline
- January 31, 2026: Final leaderboard and code verification requests
- February 7, 2026: Deadline for responding to code verification
Workshop Paper Submission
- Paper Submission Deadline: February 18, 2026
- Notification of Acceptance: March 13, 2026
- Camera-Ready Deadline: March 20, 2026
- Workshop Date: May 16, 2026
Eligibility & Participation
The CT-DEB’26 Shared Task is open to all researchers and practitioners interested in machine learning applications in healthcare. It targets individuals and teams from the ML and NLP communities who are keen to contribute to improving clinical trial design and patient outcomes.
Submission or Application Guidelines
Participants must develop predictive models using the ct-dosing-errors-benchmark dataset and submit their results via CodaBench. Code repositories are required for leaderboard eligibility. Participants are also invited to submit short papers summarizing their methodologies to the CL4Health’26 workshop.
More Information
This shared task represents a significant opportunity for researchers to engage with real-world challenges in clinical trials. By leveraging machine learning techniques, participants can contribute to the advancement of safe clinical practices and enhance patient care. The integration of AI in healthcare is crucial for developing innovative solutions that address pressing issues in clinical research.
Conclusion
The CT-DEB’26 Shared Task invites the machine learning and natural language processing communities to collaborate in addressing medication dosing errors in clinical trials. Researchers are encouraged to participate, share their insights, and contribute to this important field of study. Explore the task, engage with the community, and help shape the future of clinical trial safety.
Category: Conferences & Workshops
Tags: machine learning, clinical trials, medication errors, predictive modeling, healthcare, NLP, LREC, CodaBench, data science, clinical research
