Development and validation of a machine learning-based predictive model for assessing the 90-day prognostic outcome of patients with spontaneous intracerebral hemorrhage

被引:0
作者
Zhi Geng
Chaoyi Yang
Ziye Zhao
Yibing Yan
Tao Guo
Chaofan Liu
Aimei Wu
Xingqi Wu
Ling Wei
Yanghua Tian
Panpan Hu
Kai Wang
机构
[1] The First Affiliated Hospital of Anhui Medical University,Department of Neurology
[2] Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders,Center for Biomedical Imaging
[3] Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health,Department of Neurology, The Second People’s Hospital of Hefei
[4] University of Science and Technology of China,Institute of Artificial Intelligence
[5] Hefei Hospital Affiliated to Anhui Medical University,Department of Sleep Psychology
[6] Hefei Comprehensive National Science Center,undefined
[7] Anhui Provincial Institute of Translational Medicine,undefined
[8] Anhui Medical University,undefined
[9] The Second Hospital of Anhui Medical University,undefined
[10] Anhui Medical University,undefined
来源
Journal of Translational Medicine | / 22卷
关键词
Spontaneous intracerebral hemorrhage; Prognosis; Prediction model; Machine learning;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
相关论文
共 280 条
[1]  
Sheth KN(2022)Spontaneous intracerebral hemorrhage N Engl J Med 387 1589-1596
[2]  
Anderson CS(2013)Rapid blood-pressure lowering in patients with acute intracerebral hemorrhage N Engl J Med 368 2355-2365
[3]  
Heeley E(2009)Worldwide stroke incidence and early case fatality reported in 56 population-based studies: a systematic review Lancet Neurol 8 355-369
[4]  
Huang Y(2016)Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): a case-control study Lancet 388 761-775
[5]  
Wang J(2021)Ethnic and racial variation in intracerebral hemorrhage risk factors and risk factor burden JAMA Netw Open 4 112-2751
[6]  
Stapf C(2019)The flavonoid quercetin induces AP-1 activation in FRTL-5 thyroid cells Antioxidants 8 2742-331
[7]  
Delcourt C(2021)Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis Psychol Med 51 319-209
[8]  
Lindley R(2022)Machine learning and intracranial aneurysms: from detection to outcome prediction Acta Neurochir Suppl 134 204-3771
[9]  
Robinson T(2017)Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: comparison of machine learning and other statistical approaches JAMA Cardiol 2 1260104-55
[10]  
Lavados P(1995)Intracerebral hemorrhage versus infarction: Stroke severity, risk factors, and prognosis Ann Neurol 8 3761-967