Interpretable prediction of 3-year all-cause mortality in patients with chronic heart failure based on machine learning

被引：0
|
作者：
Chenggong Xu
Hongxia Li
Jianping Yang
Yunzhu Peng
Hongyan Cai
Jing Zhou
Wenyi Gu
Lixing Chen
机构：
[1] The First Affiliated Hospital of Kunming Medical University,College of Big Data
[2] Yunnan Agricultural University,undefined
来源：
BMC Medical Informatics and Decision Making | / 23卷
关键词：
Chronic heart failure; Mortality; Machine learning; Random forest; Permutation importance; SHAP value; Partial dependence plots;
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] Interpretable prediction of 3-year all-cause mortality in patients with chronic heart failure based on machine learning
    Xu, Chenggong
    Li, Hongxia
    Yang, Jianping
    Peng, Yunzhu
    Cai, Hongyan
    Zhou, Jing
    Gu, Wenyi
    Chen, Lixing
    BMC MEDICAL INFORMATICS AND DECISION MAKING, 2023, 23 (01)
  • [2] Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning
    Wang, Ke
    Tian, Jing
    Zheng, Chu
    Yang, Hong
    Ren, Jia
    Liu, Yanling
    Han, Qinghua
    Zhang, Yanbo
    COMPUTERS IN BIOLOGY AND MEDICINE, 2021, 137
  • [3] Interpretable machine learning prediction of all-cause mortality
    Qiu, Wei
    Chen, Hugh
    Dincer, Ayse Berceste
    Lundberg, Scott
    Kaeberlein, Matt
    Lee, Su-In
    COMMUNICATIONS MEDICINE, 2022, 2 (01):
  • [4] Interpretable machine learning prediction of all-cause mortality
    Wei Qiu
    Hugh Chen
    Ayse Berceste Dincer
    Scott Lundberg
    Matt Kaeberlein
    Su-In Lee
    Communications Medicine, 2
  • [5] Machine learning model for predicting 1-year and 3-year all-cause mortality in ischemic heart failure patients
    Cai, Anping
    Chen, Rui
    Pang, Chengcheng
    Liu, Hui
    Zhou, Yingling
    Chen, Jiyan
    Li, Liwen
    POSTGRADUATE MEDICINE, 2022, 134 (08) : 810 - 819
  • [6] Predicting 3-year all-cause mortality in patients undergoing hemodialysis using machine learning
    Okubo, Aiko
    Doi, Toshiki
    Morii, Kenichi
    Nishizawa, Yoshiko
    Yamashita, Kazuomi
    Shigemoto, Kenichiro
    Mizuiri, Sonoo
    Arakawa, Tetsuji
    Arita, Michiko
    Naito, Takayuki
    Masaki, Takao
    JOURNAL OF NEPHROLOGY, 2025,
  • [7] Predicting 3-year all-cause mortality in rectal cancer patients based on body composition and machine learning
    Li, Xiangyong
    Zhou, Zeyang
    Zhang, Xiaoyang
    Cheng, Xinmeng
    Xing, Chungen
    Wu, Yong
    FRONTIERS IN NUTRITION, 2025, 12
  • [8] Interpretable machine learning models for the prediction of all-cause mortality and time to death in hemodialysis patients
    Chen, Minjie
    Zeng, Youbing
    Liu, Mengting
    Li, Zhenghui
    Wu, Jiazhen
    Tian, Xuan
    Wang, Yunuo
    Xu, Yuanwen
    THERAPEUTIC APHERESIS AND DIALYSIS, 2024,
  • [9] Prediction of All-cause Mortality with Sepsis-associated Encephalopathy in the ICU Based on Interpretable Machine Learning
    Lu, Xiao
    Zhu, Jiang
    Gui, Jiahui
    Li, Qin
    PROCEEDINGS OF 2022 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (IEEE ICMA 2022), 2022, : 298 - 302
  • [10] Machine Learning and Prediction of All-Cause Mortality in COPD
    Moll, Matthew
    Qiao, Dandi
    Regan, Elizabeth A.
    Hunninghake, Gary M.
    Make, Barry J.
    Tal-Singer, Ruth
    McGeachie, Michael J.
    Castaldi, Peter J.
    Estepar, Raul San Jose
    Washko, George R.
    Wells, James M.
    LaFon, David
    Strand, Matthew
    Bowler, Russell P.
    Han, MeiLan K.
    Vestbo, Jorgen
    Celli, Bartolome
    Calverley, Peter
    Crapo, James
    Silverman, Edwin K.
    Hobbs, Brian D.
    Cho, Michael H.
    CHEST, 2020, 158 (03) : 952 - 964
12345