Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding.

Journal: Drug safety
Published Date: Jan 1, 2019

Abstract

INTRODUCTION: Adverse drug event (ADE) detection is a vital step towards effective pharmacovigilance and prevention of future incidents caused by potentially harmful ADEs. The electronic health records (EHRs) of patients in hospitals contain valuable information regarding ADEs and hence are an important source for detecting ADE signals. However, EHR texts tend to be noisy. Yet applying off-the-shelf tools for EHR text preprocessing jeopardizes the subsequent ADE detection performance, which depends on a well tokenized text input.

Authors

  • Susmitha Wunnava
    Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA, 01609, USA. swunnava@wpi.edu.
  • Xiao Qin
    Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA, 01609, USA.
  • Tabassum Kakar
    Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA, 01609, USA.
  • Cansu Sen
    Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA, 01609, USA.
  • Elke A Rundensteiner
    Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA, 01609, USA.
  • Xiangnan Kong
    Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA, 01609, USA.

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