Natural language processing with deep learning for medical adverse event detection from free-text medical narratives: A case study of detecting total hip replacement dislocation.

Journal: Computers in biology and medicine
Published Date: Nov 24, 2020

Abstract

BACKGROUND: Accurate and timely detection of medical adverse events (AEs) from free-text medical narratives can be challenging. Natural language processing (NLP) with deep learning has already shown great potential for analyzing free-text data, but its application for medical AE detection has been limited.

Authors

  • Alireza Borjali
    Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, Massachusetts.
  • Martin Magnéli
    Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, MA, USA; Department of Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA; Karolinska Institutet, Department of Clinical Sciences, Danderyd Hospital, Stockholm, Sweden.
  • David Shin
    School of Medicine, Loma Linda University, Loma Linda, CA, USA.
  • Henrik Malchau
    Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, MA, USA; Department of Orthopaedic Surgery, Sahlgrenska University Hospital, Sweden.
  • Orhun K Muratoglu
    Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, Massachusetts.
  • Kartik M Varadarajan
    Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, Massachusetts.

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