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A Neural Attention Model for Categorizing Patient Safety Events

  • Arman Cohan
  • Allan Fong
  • Nazli Goharian
  • Raj Ratwani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10193)

Abstract

Patient Safety Event reports are narratives describing potential adverse events to the patients and are important in identifying, and preventing medical errors. We present a neural network architecture for identifying the type of safety events which is the first step in understanding these narratives. Our proposed model is based on a soft neural attention model to improve the effectiveness of encoding long sequences. Empirical results on two large-scale real-world datasets of patient safety reports demonstrate the effectiveness of our method with significant improvements over existing methods.

Keywords

Deep learning Text categorization Medical text 

Notes

Acknowledgments

We thank the 3 anonymous reviewers for their helpful comments. This project was funded under contract/grant number Grant R01 HS023701-02 from the Agency for Healthcare Research and Quality (AHRQ), U.S. Department of Health and Human Services. The opinions expressed in this document are those of the authors and do not necessarily reflect the official position of AHRQ or the U.S. Department of Health and Human Services.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Arman Cohan
    • 1
  • Allan Fong
    • 2
  • Nazli Goharian
    • 1
  • Raj Ratwani
    • 2
  1. 1.Georgetown UniversityWashington DCUSA
  2. 2.National Center for Human Factors in HealthcareMedStar HealthWashington DCUSA

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