An interpretable hybrid machine learning approach for predicting three-month unfavorable outcomes in patients with acute ischemic stroke.

Journal: International journal of medical informatics
PMID: 39923294

Abstract

BACKGROUND: Acute ischemic stroke (AIS) is a clinical disorder caused by nontraumatic cerebrovascular disease with a high incidence, mortality, and disability rate. Most stroke survivors are left with speech and physical impairments, and emotional problems. Despite technological advances and improved treatment options, death and disability after stroke remain a major problem. Our research aims to develop interpretable hybrid machine learning (ML) models to accurately predict three-month unfavorable outcomes in patients with AIS.

Authors

  • Chen Chen
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
  • Wenkang Zhang
    Department of Cardiology, Zhongda Hospital, Southeast University, Nanjing 210009 Jiangsu, China; School of Medicine, Southeast University, Nanjing 210009 Jiangsu, China.
  • Yang Pan
    Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA.
  • Zhen Li
    PepsiCo R&D, Valhalla, NY, United States.

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