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NCT Number: NCT07808866

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

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.

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Key information

Conditions

Adverse Drug Reactions Chemically-Induced Disorders Clinical Decision Support Drug-Related Side Effects and Adverse Reactions

Sex eligibility

All sexes

Study type

Observational

Primary location

Peking University Third Hospital, Beijing, Beijing Municipality, China

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About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Cases shall involve drug categories including anti-infectives, cardiovascular agents, anti-tumor drugs, central nervous system drugs, digestive system drugs, etc. Each case must contain at least one definite adverse drug reaction (ADR) event, with complete supporting documentation (medical history, medication history, ADR occurrence process, and clinical outcome).

Exclusion criteria

  • Cases with incomplete supporting documentation lacking medical history, medication history, ADR occurrence process or clinical outcome.
  • Cases only with suspected or possible ADRs without definite clinical confirmation.

Treatment and study plan

Retrospective medical record data analysis only, no clinical intervention

Other

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.

Primary outcomes

  1. Coverage rate of known ADRs

    Time frame: Up to 8 weeks

  2. Objective question accuracy

    Time frame: Up to 24 weeks

  3. Concordance rate of predicted unknown ADRs

    Time frame: Up to 8 weeks

  4. Expert-rated subjective answer quality

    Time frame: Up to 24 weeks

Secondary outcomes

  1. Subgroup differences in ADR recognition coverage rate

    Time frame: Up to 24 weeks

  2. Inter-rater consistency

    Time frame: Up to 24 weeks

  3. Subgroup differences in answer quality score

    Time frame: Up to 24 weeks

  4. Rater acceptance scale score

    Time frame: Up to 24 weeks

Study contacts

Contact information is provided by the study sponsor or research team.

Weilong Zhang

CONTACT

[email protected]

010-82266782

Sponsors and collaborators

Lead sponsor

Peking University Third Hospital

Other

Registry information

Official study title

Multi-Agent Collaborative Framework for Adverse Drug Reaction Prediction: Evidence-Based Verification and Human-Machine Decision Comparative Study

Important dates

Study start
2026
Primary completion
2027
Study completion
2027
First posted
Sep 9, 2026
Registry last updated
Sep 9, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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