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

NCT07808866 | Recruiting

• 1 other identifier: LLSC-2026288
• Study Type: observational
• Target: 20,000
• Locations: 1 country
• Sites: 2

Brief 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.

Conditions

Adverse Drug Reactions; Clinical Decision Support

Outcome Measures

Primary Outcomes (4)

• Coverage rate of known ADRs

  Up to 8 weeks

• Objective question accuracy

  Up to 24 weeks

• Concordance rate of predicted unknown ADRs

  Up to 8 weeks

• Expert-rated subjective answer quality

  Up to 24 weeks

Secondary Outcomes (4)

• Subgroup differences in ADR recognition coverage rate

  Up to 24 weeks

• Inter-rater consistency

  Up to 24 weeks

• Subgroup differences in answer quality score

  Up to 24 weeks

• Rater acceptance scale score

  Up to 24 weeks

Interventions

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.

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

This study includes two types of research subjects: Retrospective de-identified adverse drug reaction (ADR) medical records: A total of 253 ADR consultation cases covering anti-infectives, cardiovascular agents, anti-tumor drugs, central nervous system drugs and digestive system drugs. Each case contains complete medical history, medication records, ADR occurrence process and clinical outcome data, as well as at least one clinically confirmed definite ADR event. Clinical evaluators: 20 licensed physicians or pharmacists holding intermediate or higher professional titles, with clinical pharmacy practice and regular participation in hospital ADR monitoring and consultation work.

You may qualify if:

• 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).

You may not qualify if:

• 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.

Sponsors & Collaborators

• Peking University Third Hospital: lead

Study Sites (2)

Peking University Third Hospital

Beijing, Beijing Municipality, China

RECRUITING

Peking University Third Hospital

Beijing, China

NOT YET RECRUITING

MeSH Terms

Conditions

Drug-Related Side Effects and Adverse Reactions

Condition Hierarchy (Ancestors)

Chemically-Induced Disorders

Central Study Contacts

Weilong Zhang

CONTACT

010-82266782; zhangwl2012@126.com

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Chief Physician

Study Record Dates

• First Submitted: August 18, 2026
• First Posted: September 9, 2026
• Study Start: June 19, 2026
• Primary Completion (Estimated): June 19, 2027
• Study Completion (Estimated): December 19, 2027
• Last Updated: September 9, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will not share

Locations

CN (2)