Machine learning based on nutritional assessment to predict adverse events in older inpatients with possible sarcopenia.

Journal: Aging clinical and experimental research
PMID: 39985661

Abstract

BACKGROUND: The accuracy of current tools for predicting adverse events in older inpatients with possible sarcopenia is still insufficient to develop individualized nutrition-related management strategies. The objectives were to develop a machine learning model based on nutritional assessment for the prediction of all-cause death and infectious complications.

Authors

  • Chengyu Liu
    Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
  • Hongyun Huang
    Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
  • Moxi Chen
    Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
  • Mingwei Zhu
    Department of General Surgery, Department of Hepato-bilio-pancreatic Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, 1 Dhua Road, Donghuamen Street, Dongcheng District, Beijing, 100730, China. zhumw2013@163.com.
  • Jianchun Yu
    Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.

Keywords

External Resources

Popular Topics
Recent Journals