A Novel Machine Learning Model for Predicting Stroke-Associated Pneumonia After Spontaneous Intracerebral Hemorrhage.

Journal: World neurosurgery
Published Date: Jun 4, 2024

Abstract

BACKGROUND: Pneumonia is one of the most common complications after spontaneous intracerebral hemorrhage (sICH), i.e., stroke-associated pneumonia (SAP). Timely identification of targeted patients is beneficial to reduce poor prognosis. So far, there is no consensus on SAP prediction, and application of existing predictors is limited. The aim of this study was to develop a machine learning model to predict SAP after sICH.

Authors

  • Rui Guo
    College of Chemistry&Chemical Engineering, Xiamen University, Xiamen 361005, China.
  • Siyu Yan
    Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China; West China School of Medicine, Sichuan University, Chengdu, China.
  • Yansheng Li
    DHC Mediway Technology Co., Ltd., Beijing, China.
  • Kejia Liu
    DHC Mediway Technology Co., Ltd., Beijing, China.
  • Fatian Wu
    DHC Mediway Technology Co., Ltd, Beijing, China.
  • Tianyu Feng
    DHC Mediway Technology Co., Ltd, Beijing, China.
  • Ruiqi Chen
    College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China.
  • Yi Liu
    Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China.
  • Chao You
    Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China.
  • Rui Tian
    Department of Obstetrics and Gynecology, Precision Medicine Institute, Sun Yat-sen University, Yuexiu, Guangzhou, Guangdong, China.

Keywords

External Resources

Popular Topics
Recent Journals