Extracting seizure frequency from epilepsy clinic notes: a machine reading approach to natural language processing.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Apr 13, 2022

Abstract

OBJECTIVE: Seizure frequency and seizure freedom are among the most important outcome measures for patients with epilepsy. In this study, we aimed to automatically extract this clinical information from unstructured text in clinical notes. If successful, this could improve clinical decision-making in epilepsy patients and allow for rapid, large-scale retrospective research.

Authors

  • Kevin Xie
    Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Ryan S Gallagher
    Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Erin C Conrad
    Department of Neurology, Penn Epilepsy Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Chadric O Garrick
    Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Steven N Baldassano
  • John M Bernabei
    Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Peter D Galer
    Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
  • Nina J Ghosn
    Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Adam S Greenblatt
    Department of Neurology, Penn Epilepsy Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Tara Jennings
    Department of Neurology, Penn Epilepsy Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Alana Kornspun
    Department of Neurology, Penn Epilepsy Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Catherine V Kulick-Soper
    Department of Neurology, Penn Epilepsy Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Jal M Panchal
    Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Akash R Pattnaik
    Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.
  • Brittany H Scheid
    Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Danmeng Wei
    Department of Neurology, Penn Epilepsy Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Micah Weitzman
    Department of Electrical and Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Ramya Muthukrishnan
    Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Computer Science, University of Pennsylvania, Philadelphia, PA 19104, USA.
  • Joongwon Kim
    Department of Computer and Information Science, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Brian Litt
    Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States of America.
  • Colin A Ellis
    Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Dan Roth
    Department of Computer and Information Science, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

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