MADEx: A System for Detecting Medications, Adverse Drug Events, and Their Relations from Clinical Notes.

Journal: Drug safety
Published Date:

Abstract

INTRODUCTION: Early detection of adverse drug events (ADEs) from electronic health records is an important, challenging task to support pharmacovigilance and drug safety surveillance. A well-known challenge to use clinical text for detection of ADEs is that much of the detailed information is documented in a narrative manner. Clinical natural language processing (NLP) is the key technology to extract information from unstructured clinical text.

Authors

  • Xi Yang
    Department of Health Outcomes and Biomedical Informatics.
  • Jiang Bian
    Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, United States of America.
  • Yan Gong
    Cardio-Oncology Working Group, University of Florida Health Cancer Center, Gainesville, FL, USA.
  • William R Hogan
    Department of Health Outcomes and Biomedical Informatics.
  • Yonghui Wu
    Department of Health Outcomes and Biomedical Informatics.