A machine learning method for predicting the probability of MODS using only non-invasive parameters.

Journal: Computer methods and programs in biomedicine
PMID: 36384060

Abstract

OBJECTIVES: Timely and accurate prediction of multiple organ dysfunction syndrome (MODS) is essential for the rescue and treatment of trauma patients However, existing methods are invasive, easily affected by artifacts and can be difficult to perform in a pre-hospital setting. We aim to develop prediction models for patients with MODS using only non-invasive parameters.

Authors

  • Guanjun Liu
    School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China; Hubei Provincial Key Laboratory of Digital Watershed Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
  • JiaMeng Xu
    Institute of Medical Support, Academy of Military Sciences, Tianjin, China.
  • Chengyi Wang
    School of Life Sciences, Tiangong University, 399 Binshui West Road, Tianjin 300387, China.
  • Ming Yu
    College of Computer and Control Engineering, Northeast Forestry University, Harbin, Heilongjiang Province, China.
  • Jing Yuan
    School of Acu-Mox and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
  • Feng Tian
    Bioinformatics Graduate Program, and Department of Biomedical Engineering, Boston. University, 24 Cummington Mall, Boston, MA 02215, USA.
  • Guang Zhang
    Department of Health Management, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.

Keywords

External Resources

Popular Topics
Recent Journals