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Adverse Drug Reaction Discovery from Electronic Health Records with Deep Neural Networks.

Wei Zhang1, Peggy Peissig2, Zhaobin Kuang3

  • 1Computer Sciences Department, University of Wisconsin-Madison.

Proceedings of the ACM Conference on Health, Inference, and Learning
|December 7, 2020
PubMed
Summary

This study introduces a deep learning framework, Neural Self-Controlled Case Series (NSCCS), for discovering adverse drug reactions (ADRs) from electronic health records (EHRs). NSCCS improves ADR identification by efficiently adjusting for patient differences.

Keywords:
Adverse Drug Reaction DiscoveryDeep Neural NetworksElectronic Health RecordsSelf-Controlled Case Series

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Area of Science:

  • Computational biology
  • Pharmacovigilance
  • Machine learning in healthcare

Background:

  • Adverse drug reactions (ADRs) are significant clinical events.
  • Electronic health records (EHRs) provide vast longitudinal data for ADR discovery.
  • Traditional methods struggle with the complexity and scale of EHR data.

Purpose of the Study:

  • To develop a deep learning framework for enhanced ADR discovery from EHRs.
  • To introduce the Neural Self-Controlled Case Series (NSCCS) model.
  • To leverage the self-controlled case series design within a deep learning approach.

Main Methods:

  • Developed NSCCS, a deep learning framework for ADR discovery.
  • Implemented a rigorous self-controlled case series design to implicitly adjust for individual heterogeneity.
  • Applied the NSCCS model to a large-scale, real-world EHR dataset.

Main Results:

  • NSCCS demonstrated robust performance in identifying ADRs.
  • The framework effectively adjusted for time-invariant confounding factors.
  • Empirical experiments confirmed superior performance on a benchmark ADR discovery task.

Conclusions:

  • NSCCS is a powerful deep learning tool for ADR discovery from EHRs.
  • The model's design enhances the ability to identify drug-adverse condition associations.
  • This approach offers a more robust method for pharmacovigilance using big data.