Early prediction of hemodynamic interventions in the intensive care unit using machine learning

被引:0
|
作者
Asif Rahman
Yale Chang
Junzi Dong
Bryan Conroy
Annamalai Natarajan
Takahiro Kinoshita
Francesco Vicario
Joseph Frassica
Minnan Xu-Wilson
机构
[1] Philips Research North America,Institute for Medical Engineering and Science
[2] Massachusetts Institute of Technology,undefined
来源
Critical Care | / 25卷
关键词
Hemodynamics; Vasoactive therapy; Machine learning; Clinical decision support;
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] Early prediction of hemodynamic interventions in the intensive care unit using machine learning
    Rahman, Asif
    Chang, Yale
    Dong, Junzi
    Conroy, Bryan
    Natarajan, Annamalai
    Kinoshita, Takahiro
    Vicario, Francesco
    Frassica, Joseph
    Xu-Wilson, Minnan
    CRITICAL CARE, 2021, 25 (01)
  • [2] Early prediction of MODS interventions in the intensive care unit using machine learning
    Liu, Chang
    Yao, Zhenjie
    Liu, Pengfei
    Tu, Yanhui
    Chen, Hu
    Cheng, Haibo
    Xie, Lixin
    Xiao, Kun
    JOURNAL OF BIG DATA, 2023, 10 (01)
  • [3] Early prediction of MODS interventions in the intensive care unit using machine learning
    Chang Liu
    Zhenjie Yao
    Pengfei Liu
    Yanhui Tu
    Hu Chen
    Haibo Cheng
    Lixin Xie
    Kun Xiao
    Journal of Big Data, 10
  • [4] Early prediction of circulatory failure in the intensive care unit using machine learning
    Hyland, Stephanie L.
    Faltys, Martin
    Huser, Matthias
    Lyu, Xinrui
    Gumbsch, Thomas
    Esteban, Cristobal
    Bock, Christian
    Horn, Max
    Moor, Michael
    Rieck, Bastian
    Zimmermann, Marc
    Bodenham, Dean
    Borgwardt, Karsten
    Ratsch, Gunnar
    Merz, Tobias M.
    NATURE MEDICINE, 2020, 26 (03) : 364 - +
  • [5] Early prediction of circulatory failure in the intensive care unit using machine learning
    Stephanie L. Hyland
    Martin Faltys
    Matthias Hüser
    Xinrui Lyu
    Thomas Gumbsch
    Cristóbal Esteban
    Christian Bock
    Max Horn
    Michael Moor
    Bastian Rieck
    Marc Zimmermann
    Dean Bodenham
    Karsten Borgwardt
    Gunnar Rätsch
    Tobias M. Merz
    Nature Medicine, 2020, 26 : 364 - 373
  • [6] Early prediction of intensive care unit admission in emergency department patients using machine learning
    Pandey, Dinesh
    Jahanabadi, Hossein
    D'Arcy, Jack
    Doherty, Suzanne
    Vo, Hung
    Jones, Daryl
    Bellomo, Rinaldo
    AUSTRALIAN CRITICAL CARE, 2025, 38 (01)
  • [7] Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients
    Alanazi, Abdullah
    Aldakhil, Lujain
    Aldhoayan, Mohammed
    Aldosari, Bakheet
    MEDICINA-LITHUANIA, 2023, 59 (07):
  • [8] MACHINE LEARNING PREDICTION OF INTENSIVE CARE UNIT DELIRIUM
    Gong, Kirby
    Lu, Ryan
    Bergamaschi, Teya
    Sanyal, Akaash
    Guo, Joanna
    Kim, Hanbiehn
    Stevens, Robert
    CRITICAL CARE MEDICINE, 2021, 49 (01) : 14 - 14
  • [9] Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning
    Oh, Jooyoung
    Cho, Dongrae
    Park, Jaesub
    Na, Se Hee
    Kim, Jongin
    Heo, Jaeseok
    Shin, Cheung Soo
    Kim, Jae-Jin
    Park, Jin Young
    Lee, Boreom
    PHYSIOLOGICAL MEASUREMENT, 2018, 39 (03)
  • [10] Point of Care Prediction of Maternal Admission to the Intensive Care Unit Using Machine Learning
    Ganguli, Reetam
    Gupta, Megha
    Anderson, Katie
    Wagner, Stephen M.
    AMERICAN JOURNAL OF OBSTETRICS AND GYNECOLOGY, 2023, 228 (01) : S216 - S216