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Optimising Pharmacovigilance Efficiency with MLIT (Machine Learning for Intelligent Triage): A Tool for Statistical
Luciano Ciccarelli1, Olivia Mahaux2, Christie Roshan3
1GSK, Siena, Italy. luciano.x.ciccarelli@gsk.com.
Drug Safety
|August 10, 2026
Summary
An explainable Machine Learning for Intelligent Triage (MLIT) tool improved pharmacovigilance by efficiently reviewing drug and vaccine safety alerts. The MLIT tool enhanced signal detection and reduced triage time by 24%, while maintaining regulatory compliance.
Area of Science:
- Pharmacovigilance and Drug Safety
- Machine Learning in Healthcare
- Regulatory Science
Background:
- Pharmacovigilance relies on timely identification of adverse reactions from complex data.
- Current quantitative signal detection methods generate numerous alerts, overwhelming pharmacovigilance teams.
- Manual alert triage is inefficient, resource-intensive, and prone to variability.
Purpose of the Study:
- To design, develop, and prospectively evaluate an explainable Machine Learning for Intelligent Triage (MLIT) tool.
- To assist pharmacovigilance teams in reviewing statistical alerts for vaccine and drug portfolios.
- To enhance signal detection performance, improve operational efficiency, and maintain regulatory compliance.
Main Methods:
- Utilized alert and individual case safety report data.
- Employed eXtreme Gradient Boosting (XGBoost) as the optimal machine learning algorithm.
- Refined models using Shapley Additive Explanations for explainability and conducted prospective validation.
Main Results:
- The vaccine MLIT model achieved an F1 score of 0.81 and accuracy of 0.79.
- 92% of vaccine alerts aligned with the model's top prediction; 98% within the top 3.
- Safety reviewers reported a 24% reduction in triage time; comparable results for drugs.
Conclusions:
- Machine learning tools can significantly improve pharmacovigilance efficiency and transparency.
- MLIT demonstrated high concordance with expert decisions and substantial time savings.
- Human oversight remains crucial for low-confidence predictions, with ongoing refinement needed for broader implementation.
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