Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated encephalopathy

被引：0
|
作者：
Liwei Peng
Chi Peng
Fan Yang
Jian Wang
Wei Zuo
Chao Cheng
Zilong Mao
Zhichao Jin
Weixin Li
机构：
[1] Tangdu Hospital,Department of Neurosurgery
[2] Fourth Military Medical University,Department of Health Statistics
[3] Second Military Medical University,Institute of Pathology and Southwest Cancer Center, Southwest Hospital
[4] Third Military Medical University (Army Medical University),undefined
来源：
BMC Medical Research Methodology | / 22卷
关键词：
Machine learning; Model interpretation; Sepsis-associated encephalopathy; SAE; Web-based calculator;
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] Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated encephalopathy
    Peng, Liwei
    Peng, Chi
    Yang, Fan
    Wang, Jian
    Zuo, Wei
    Cheng, Chao
    Mao, Zilong
    Jin, Zhichao
    Li, Weixin
    BMC MEDICAL RESEARCH METHODOLOGY, 2022, 22 (01)
  • [2] Development of a nomogram to predict 30-day mortality of patients with sepsis-associated encephalopathy: a retrospective cohort study
    Yang, Yang
    Liang, Shengru
    Geng, Jie
    Wang, Qiuhe
    Wang, Pan
    Cao, Yuan
    Li, Rong
    Gao, Guodong
    Li, Lihong
    JOURNAL OF INTENSIVE CARE, 2020, 8 (01)
  • [3] Development of a nomogram to predict 30-day mortality of patients with sepsis-associated encephalopathy: a retrospective cohort study
    Yang Yang
    Shengru Liang
    Jie Geng
    Qiuhe Wang
    Pan Wang
    Yuan Cao
    Rong Li
    Guodong Gao
    Lihong Li
    Journal of Intensive Care, 8
  • [4] Mechanical Learning for Prediction of Sepsis-Associated Encephalopathy
    Zhao, Lina
    Wang, Yunying
    Ge, Zengzheng
    Zhu, Huadong
    Li, Yi
    FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2021, 15
  • [5] 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
  • [6] Prediction of 30-day mortality for ICU patients with Sepsis-3
    Yu, Zhijiang
    Ashrafi, Negin
    Li, Hexin
    Alaei, Kamiar
    Pishgar, Maryam
    BMC MEDICAL INFORMATICS AND DECISION MAKING, 2024, 24 (01)
  • [7] Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3
    Rahman, Md. Sohanur
    Islam, Khandaker Reajul
    Prithula, Johayra
    Kumar, Jaya
    Mahmud, Mufti
    Alam, Mohammed Fasihul
    Reaz, Mamun Bin Ibne
    Alqahtani, Abdulrahman
    Chowdhury, Muhammad E. H.
    BMC MEDICAL INFORMATICS AND DECISION MAKING, 2024, 24 (01)
  • [8] Prediction and risk assessment of sepsis-associated encephalopathy in ICU based on interpretable machine learning
    Xiao Lu
    Hongyu Kang
    Dawei Zhou
    Qin Li
    Scientific Reports, 12
  • [9] Prediction and risk assessment of sepsis-associated encephalopathy in ICU based on interpretable machine learning
    Lu, Xiao
    Kang, Hongyu
    Zhou, Dawei
    Li, Qin
    SCIENTIFIC REPORTS, 2022, 12 (01)
  • [10] Interpretable prediction of 30-day mortality in patients with acute pancreatitis based on machine learning and SHAP
    Li, Xiaojing
    Tian, Yueqin
    Li, Shuangmei
    Wu, Haidong
    Wang, Tong
    BMC MEDICAL INFORMATICS AND DECISION MAKING, 2024, 24 (01)
12345