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

Yue YuZhibin ChenYong YangJiajun ZhangYan Wang
Published in: Epilepsy research (2024)
Our study confirmed the feasibility of applying the ML method to use easy-to-obtain variables for accurate prediction of PSE and provided improved strategies and effective resource allocation for high-risk patients. In addition, the SHAP method could improve model transparency and make it easier for clinicians to grasp the prediction model's reliability.
Keyphrases
  • machine learning
  • end stage renal disease
  • ejection fraction
  • newly diagnosed
  • chronic kidney disease
  • peritoneal dialysis
  • prognostic factors
  • high resolution
  • deep learning
  • big data
  • mass spectrometry