Machine learning algorithms for risk factor selection with application to 60-day sepsis morbidity risk for a geriatric hip fracture cohort.

Journal: BMC geriatrics
Published Date: Aug 4, 2025

Abstract

BACKGROUND: Sepsis after hip fracture in elderly people is a risk factor for mortality. The purpose of this study was to screen for risk factors for 60-day sepsis morbidity after hip fracture and to establish a predictive model using various machine learning algorithms.

Authors

  • Zhe Xu
    Thayer School of Engineering at Dartmouth College Hanover NH USA john.zhang@dartmouth.edu.
  • Ruguo Zhang
    Department of Orthopedics, Guihang Guiyang 300 Hospital, Guiyang, 550004, China. 2643448603@qq.com.
  • Qiuhan Chen
    Guizhou Medical University, Guiyang, 550004, China.
  • Guoxuan Peng
    Department of Emergency, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, China.
  • Shanpeng Luo
    Department of Orthopedics, The Fifth Hospital of Guiyang City, Guiyang, 550004, China.
  • Chen Liu
    Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China.
  • Ling Zeng
    Department of Trauma Medical Center, Daping Hospital, State Key Laboratory of Trauma, Burns and Combined Injury, Army Medical University, Chongqing, 400042, China. zengling_1025@tmmu.edu.cn.
  • Jin Deng

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