Development and validation of a deep learning model to predict the survival of patients in ICU.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 35751440

Abstract

BACKGROUND: Patients in the intensive care unit (ICU) are often in critical condition and have a high mortality rate. Accurately predicting the survival probability of ICU patients is beneficial to timely care and prioritizing medical resources to improve the overall patient population survival. Models developed by deep learning (DL) algorithms show good performance on many models. However, few DL algorithms have been validated in the dimension of survival time or compared with traditional algorithms.

Authors

  • Hai Tang
    Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
  • Zhuochen Jin
    College of Design and Innovation, Tongji University, Shanghai, China.
  • Jiajun Deng
    Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
  • Yunlang She
    Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
  • Yifan Zhong
    Division of Thyroid Surgery, Jilin Provincial Key Laboratory of Surgical Translational Medicine, Jilin Provincial Precision Medicine Laboratory of Molecular Biology and Translational Medicine On Differentiated Thyroid Carcinoma, China-Japan Union Hospital Of Jilin University, Changchun, 130000, People's Republic of China.
  • Weiyan Sun
    Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
  • Yijiu Ren
    Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
  • Nan Cao
    College of Design and Innovation, Tongji University, Shanghai, China.
  • Chang Chen
    Biomass Energy and Environmental Engineering Research Center, College of Chemical Engineering, Beijing University of Chemical Technology, 505 Zonghe Building A, 15 North 3rd Ring East Road, Beijing, 100029, China. chenchang@mail.buct.edu.cn.

Keywords

External Resources

Popular Topics
Recent Journals