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Feasibility of Causality-Aware Machine Learning for Drug Safety on OMOP-CDM
Alexandros Rekkas1, Nikolas Theologitis2, Anastasia Farmaki1
1Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
This study introduces a novel Artificial Intelligence (AI) pipeline for pharmacovigilance (PV) signal analysis. It uses causal machine learning and real-world data to improve drug safety monitoring.
Area of Science:
- Pharmacovigilance and AI
- Causal Machine Learning in Healthcare
Background:
- Real-world healthcare data and AI are crucial for modern pharmacovigilance (PV).
- Existing initiatives focus on scalable PV pipelines using common data models like OMOP-CDM.
Purpose of the Study:
- To propose an AI-driven pipeline for pharmacovigilance signal analysis.
- To integrate causal machine learning principles into PV data analysis.
Main Methods:
- Developed a pipeline combining standard machine learning (ML) with Directed Acyclic Graph (DAG)-informed feature selection.
- Applied causal machine learning principles for robust PV signal detection.
- Utilized real-world data from Papageorgiou General Hospital.
Main Results:
- Demonstrated the proposed pipeline's effectiveness in analyzing PV data.
- Showcased the integration of causal ML for enhanced signal identification.
- Validated the approach using a specific hospital's healthcare data.
Conclusions:
- The proposed causal ML pipeline offers a promising approach for AI-enhanced pharmacovigilance.
- This methodology can improve the analysis of real-world healthcare data for drug safety.
- The study highlights the potential of DAG-informed feature selection in PV.
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