Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest)

被引：0
|
作者：
Luming Zhang
Tao Huang
Fengshuo Xu
Shaojin Li
Shuai Zheng
Jun Lyu
Haiyan Yin
机构：
[1] The First Affiliated Hospital of Jinan University,Intensive Care Unit
[2] The First Affiliated Hospital of Jinan University,Department of Clinical Research
[3] Xi’an Jiaotong University Health Science Center,School of Public Health
[4] The First Affiliated Hospital of Jinan University,Department of Orthopaedics
[5] Shannxi University of Chinese Medicine,School of Public Health
来源：
BMC Emergency Medicine | / 22卷
关键词：
Machine learning; Random survival forest; Elderly; Sepsis; Prognosis;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [1] Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest)
    Zhang, Luming
    Huang, Tao
    Xu, Fengshuo
    Li, Shaojin
    Zheng, Shuai
    Lyu, Jun
    Yin, Haiyan
    [J]. BMC EMERGENCY MEDICINE, 2022, 22 (01)
  • [2] Prediction of prognosis and survival of patients with gastric cancer by a weighted improved random forest model: an application of machine learning in medicine
    Xu, Cheng
    Wang, Jing
    Zheng, Tianlong
    Cao, Yue
    Ye, Fan
    [J]. ARCHIVES OF MEDICAL SCIENCE, 2022, 18 (05) : 1208 - 1220
  • [3] Machine learning-based prediction of survival prognosis in cervical cancer
    Dongyan Ding
    Tingyuan Lang
    Dongling Zou
    Jiawei Tan
    Jia Chen
    Lei Zhou
    Dong Wang
    Rong Li
    Yunzhe Li
    Jingshu Liu
    Cui Ma
    Qi Zhou
    [J]. BMC Bioinformatics, 22
  • [4] Machine learning-based prediction of survival prognosis in cervical cancer
    Ding, Dongyan
    Lang, Tingyuan
    Zou, Dongling
    Tan, Jiawei
    Chen, Jia
    Zhou, Lei
    Wang, Dong
    Li, Rong
    Li, Yunzhe
    Liu, Jingshu
    Ma, Cui
    Zhou, Qi
    [J]. BMC BIOINFORMATICS, 2021, 22 (01)
  • [5] Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma
    Kaijiong Zhang
    Bo Ye
    Lichun Wu
    Sujiao Ni
    Yang Li
    Qifeng Wang
    Peng Zhang
    Dongsheng Wang
    [J]. Scientific Reports, 13
  • [6] LONG-TERM SURVIVAL PREDICTION IN EARLY BREAST CANCER: A MACHINE LEARNING APPROACH WITH RANDOM SURVIVAL FOREST
    Yoon, H.
    Han, S.
    Suh, H. S.
    Park, C.
    [J]. VALUE IN HEALTH, 2024, 27 (06) : S268 - S268
  • [7] Machine Learning Applied to survival prediction of elderly cancer patients: Systematic Review
    Lacerda, Tiago Beltrao
    Medeiros, Alberto
    Perez, Regis Batista
    Cavalcanti Furtado, Ana Paula
    [J]. 2020 15TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI'2020), 2020,
  • [8] EFFECT OF A MACHINE LEARNING-BASED SEVERE SEPSIS PREDICTION ALGORITHM ON PATIENT SURVIVAL
    Barton, Chris
    Shimabakuru, David
    Feldman, Mitchel
    Mataraso, Samson
    Das, Ritankar
    [J]. CRITICAL CARE MEDICINE, 2018, 46 (01) : 699 - 699
  • [9] Machine learning-based prognosis signature for survival prediction of patients with clear cell renal cell carcinoma
    Chen, Siteng
    Guo, Tuanjie
    Zhang, Encheng
    Wang, Tao
    Jiang, Guangliang
    Wu, Yishuo
    Wang, Xiang
    Na, Rong
    Zhang, Ning
    [J]. HELIYON, 2022, 8 (09)
  • [10] A prediction model based on random survival forest analysis of the overall survival of elderly female papillary thyroid carcinoma patients: a SEER-based study
    Lun, Yuqiang
    Yuan, Hao
    Ma, Pengwei
    Chen, Jiawei
    Lu, Peiheng
    Wang, Weilong
    Liang, Rui
    Zhang, Junjun
    Gao, Wei
    Ding, Xuerui
    Li, Siyu
    Wang, Zi
    Guo, Jianing
    Lu, Lianjun
    [J]. ENDOCRINE, 2024, 85 (02) : 598 - 600
12345