Supervised machine learning compared to large language models for identifying functional seizures from medical records.

Journal: Epilepsia
Published Date: Feb 17, 2025

Abstract

OBJECTIVE: The Functional Seizures Likelihood Score (FSLS) is a supervised machine learning-based diagnostic score that was developed to differentiate functional seizures (FS) from epileptic seizures (ES). In contrast to this targeted approach, large language models (LLMs) can identify patterns in data for which they were not specifically trained. To evaluate the relative benefits of each approach, we compared the diagnostic performance of the FSLS to two LLMs: ChatGPT and GPT-4.

Authors

  • Wesley T Kerr
    Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Katherine N McFarlane
    Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Gabriela Figueiredo Pucci
    Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Danielle R Carns
    Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Alex Israel
    Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Lianne Vighetti
    Department of Social Work, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Page B Pennell
    Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • John M Stern
    Department of Neurology, University of California, Los Angeles, Los Angeles, California, USA.
  • Zongqi Xia
    Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Yanshan Wang
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.

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