Development and validation of an interpretable machine learning model for predicting post-stroke epilepsy.

Journal: Epilepsy research
Published Date: Jun 28, 2024

Abstract

BACKGROUND: Epilepsy is a serious complication after an ischemic stroke. Although two studies have developed prediction model for post-stroke epilepsy (PSE), their accuracy remains insufficient, and their applicability to different populations is uncertain. With the rapid advancement of computer technology, machine learning (ML) offers new opportunities for creating more accurate prediction models. However, the potential of ML in predicting PSE is still not well understood. The purpose of this study was to develop prediction models for PSE among ischemic stroke patients.

Authors

  • Yue Yu
    Department of Mathematics, Lehigh University, Bethlehem, PA, USA.
  • Zhibin Chen
    Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.
  • Yong Yang
    Department of Radiation Oncology, Stanford University, CA, USA.
  • Jiajun Zhang
  • Yan Wang
    College of Animal Science and Technology, Beijing University of Agriculture, Beijing, China.

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