Predictive Value of Machine Learning Models for Cerebral Edema Risk in Stroke Patients: A Meta-Analysis.

Journal: Brain and behavior
PMID: 39778917

Abstract

INTRODUCTION: Stroke patients are at high risk of developing cerebral edema, which can have severe consequences. However, there are currently few effective tools for early identification or prediction of this risk. As machine learning (ML) is increasingly used in clinical practice, its effectiveness in predicting cerebral edema risk in stroke patients has been explored. Nonetheless, the lack of systematic evidence on its predictive value challenges the update of simple and user-friendly risk assessment tools. Therefore, we conducted a systematic review to evaluate the predictive utility of ML for cerebral edema in stroke patients.

Authors

  • Qi Deng
    College of Economics and Management, China-Africa International Business School, Zhejiang Normal University, Jinhua, China.
  • Yu Yang
    Department of Obstetrics & Gynecology, the First Affiliated Hospital of Xi'an Jiaotong University, Xian, Shaanxi, China.
  • Hongyu Bai
    Department of General Surgery, Tianjin Kanghui Hospital, Tianjin, China.
  • Fei Li
    Institute for Precision Medicine, Tsinghua University, Beijing, China.
  • Wenluo Zhang
    Department of Neurology, PKUCare Rehabilitation Hospital, Beijing, China.
  • Rong He
    Department of Neurology, PKUCare Rehabilitation Hospital, Beijing, China.
  • Yuming Li
    Department of Electronic Engineering, City University of Hong Kong, Hong Kong 201337.

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