Machine learning-based predictive model for post-stroke dementia.

Journal: BMC medical informatics and decision making
PMID: 39529118

Abstract

BACKGROUND: Post-stroke dementia (PSD), a common complication, diminishes rehabilitation efficacy and affects disease prognosis in stroke patients. Many factors may be related to PSD, including demographic, comorbidities, and examination characteristics. However, most existing methods are qualitative evaluations of independent factors, which ignore the interaction amongst various factors. Therefore, the purpose of this study is to explore the applicability of machine learning (ML) methods for predicting PSD.

Authors

  • Zemin Wei
    Department of Geriatrics, Shaoxing People's Hospital, Shaoxing, Zhejiang, P. R. China.
  • Mengqi Li
    School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China.
  • Chenghui Zhang
    School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China.
  • Jinli Miao
    The Yangtze River Delta Biological Medicine Research and Development Center of Zhejiang Province, Yangtze Delta Region Institution of Tsinghua University, Hangzhou, 314006, Zhejiang, P.R. China.
  • Wenmin Wang
    The Yangtze River Delta Biological Medicine Research and Development Center of Zhejiang Province, Yangtze Delta Region Institution of Tsinghua University, Hangzhou, 314006, Zhejiang, P.R. China.
  • Hong Fan
    Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, People's Republic of China.

Keywords

External Resources

Popular Topics
Recent Journals