A Pilot, Predictive Surveillance Model in Pharmacovigilance Using Machine Learning Approaches.

Journal: Advances in therapy
Published Date: May 5, 2024

Abstract

INTRODUCTION: The identification of a new adverse event (AE) caused by a drug product is one of the key activities in the pharmaceutical industry to ensure the safety profile of a drug product. Machine learning (ML) has the potential to assist with signal detection and supplement traditional pharmacovigilance (PV) surveillance methods. This pilot ML modeling study was designed to detect potential safety signals for two AbbVie products and test the model's capability of detecting safety signals earlier than humans.

Authors

  • Rosa De Abreu Ferreira
    Medical Safety Evaluation, Pharmacovigilance and Patient Safety, Epidemiology, and Research and Development Quality Assurance, AbbVie, Inc., North Chicago, IL, USA.
  • Sheng Zhong
    1Department of Neurosurgery and Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Charlotte Moureaud
    Safety Data Sciences, Pharmacovigilance and Patient Safety, Epidemiology, and Research and Development Quality Assurance, AbbVie, Inc., North Chicago, IL, USA. charlotte.moureaud@abbvie.com.
  • Michelle T Le
    Medication Safety Fellow, Purdue University College of Pharmacy, West Lafayette, IN, USA.
  • Adrienne Rothstein
    Medical Safety Evaluation, Pharmacovigilance and Patient Safety, Epidemiology, and Research and Development Quality Assurance, AbbVie, Inc., North Chicago, IL, USA.
  • Xiaomeng Li
  • Li Wang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Meenal Patwardhan
    Medical Safety Evaluation, Pharmacovigilance and Patient Safety, Epidemiology, and Research and Development Quality Assurance, AbbVie, Inc., North Chicago, IL, USA.

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