Traditional and machine learning models for predicting haemorrhagic transformation in ischaemic stroke: a systematic review and meta-analysis.

Journal: Systematic reviews
PMID: 39987097

Abstract

BACKGROUND: Haemorrhagic transformation (HT) is a severe complication after ischaemic stroke, but identifying patients at high risks remains challenging. Although numerous prediction models have been developed for HT following thrombolysis, thrombectomy, or spontaneous occurrence, a comprehensive summary is lacking. This study aimed to review and compare traditional and machine learning-based HT prediction models, focusing on their development, validation, and diagnostic accuracy.

Authors

  • Yanan Wang
    Vasculocardiology Department, The Third People's Hospital of Datong, Datong, Shanxi, China.
  • Zengyi Zhang
    West China School of Medicine, Sichuan University, Chengdu, Sichuan, China.
  • Zhimeng Zhang
    Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050, Beijing, China.
  • Xiaoying Chen
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
  • Junfeng Liu
    Indiana University-Purdue University, Indianapolis , 723 West Michigan St., SL 280, Indianapolis, Indiana 46202, United States.
  • Ming Liu
    School of Land Engineering, Chang'an University, Xi'an 710064, China; Xi'an Key Laboratory of Territorial Spatial Information, School of Land Engineering, Chang'an University, Xi'an 710064, China. Electronic address: mingliu@chd.edu.cn.

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