Predicting functional outcomes of patients with spontaneous intracerebral hemorrhage based on explainable machine learning models: a multicenter retrospective study.

Journal: Frontiers in neurology
Published Date: Jan 10, 2025

Abstract

BACKGROUND: Spontaneous intracerebral hemorrhage (SICH) is the second most common cause of cerebrovascular disease after ischemic stroke, with high mortality and disability rates, imposing a significant economic burden on families and society. This retrospective study aimed to develop and evaluate an interpretable machine learning model to predict functional outcomes 3 months after SICH.

Authors

  • Bin Pan
    Department of Emergency Intensive Care Unit, Changshu Hospital Affiliated to Soochow University, Changshu, China.
  • Fengda Li
    Department of Neurosurgery, Changshu Hospital Affiliated to Soochow University, Changshu, China.
  • Chuanghong Liu
    Department of Neurosurgery, Changshu Hospital Affiliated to Soochow University, Changshu, China.
  • Zeyi Li
    School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China.
  • Chengfa Sun
    Department of Neurosurgery, Changshu No.2 People's Hospital, The Affiliated Changshu Hospital of Nantong University, Changshu, China.
  • Kaijian Xia
    Intelligent Medical Technology Research Center, Changshu Hospital Affiliated to Soochow University, Changshu, China.
  • Hong Xu
    Department of Neurosurgery, Changshu Hospital Affiliated to Soochow University, Changshu, China.
  • Gang Kong
    Department of Neurosurgery, Changshu Hospital Affiliated to Soochow University, Changshu, China.
  • Longyuan Gu
    Department of Neurosurgery, Ji'an Central People's Hospital, Ji'an, China.
  • Kaiyuan Cheng
    Department of Neurosurgery, Changshu Hospital Affiliated to Soochow University, Changshu, China.

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