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Multi-Agent Collaborative ADR Prediction With Human-Machine Decision Comparison
Sponsor: Peking University Third Hospital
Summary
This study develops a multi-agent collaborative prediction model to forecast adverse drug reactions using real-world clinical medical records. It validates model performance via evidence-based data and compares decision outputs between the AI model and clinical physicians, aiming to improve early identification of drug adverse events. Only de-identified historical medical data will be analyzed; no new clinical interventions will be conducted, with no additional risks to participants.
Official title: Multi-Agent Collaborative Framework for Adverse Drug Reaction Prediction: Evidence-Based Verification and Human-Machine Decision Comparative Study
Key Details
Gender
All
Age Range
Any - Any
Study Type
OBSERVATIONAL
Enrollment
20000
Start Date
2026-06-19
Completion Date
2027-12-19
Last Updated
2026-09-09
Healthy Volunteers
No
Interventions
Retrospective medical record data analysis only, no clinical intervention
This study only analyzes de-identified historical electronic medical record data to build a multi-agent AI prediction model for adverse drug reactions. No drugs, medical devices, or clinical treatment interventions will be applied to any human subjects.
Locations (2)
Peking University Third Hospital
Beijing, Beijing Municipality, China
Peking University Third Hospital
Beijing, China