Multi-Agent Collaborative ADR Prediction With Human-Machine Decision Comparison

Overview

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.

This observational study first retrospectively collects desensitization cases related to adverse drug reactions to construct a predictive model, and then prospectively enrolls patients to evaluate model efficacy and conduct comparative research with expert blind assessment.

Conditions

Interventions