Evaluation of repeated machine learning-based phenotyping in patients with cardiogenic shock

被引：0
|
作者：
Zweck, E. [1 ]
Kanwar, M. [2 ]
Li, S. [3 ]
Sinha, S. S. [4 ]
Garan, A. R. [5 ]
Hernandez-Montfort, J. [6 ]
Abraham, J. [7 ]
Polzin, A. [1 ]
Kelm, M. [1 ]
Burkhoff, D. [8 ]
Kapur, N. K. [9 ]
机构：
[1] Univ Hosp Duesseldorf, Dusseldorf, Germany
[2] Allegheny Gen Hosp, Pittsburgh, PA USA
[3] Med City Healthcare, Dallas, TX USA
[4] Inova Heart & Vasc Inst, Falls Church, VA USA
[5] Beth Israel Deaconess Med Ctr, Boston, MA USA
[6] Baylor Scott & White Hlth, Temple, TX USA
[7] Providence Heart & Vasc Inst, Ctr Cardiovasc Analyt Res & Data Sci, Portland, OR USA
[8] Cardiovasc Res Fdn, New York, NY USA
[9] Tufts Med Ctr Inc, Cardiovasc Ctr, Boston, MA USA
来源：
EUROPEAN HEART JOURNAL | 2024年 / 45卷
关键词：
D O I：
10.1093/eurheartj/ehae666.1697
中图分类号：
R5 [内科学];
学科分类号：
1002 ; 100201 ;
摘要：
引用
收藏
页数：2
相关论文
共 50 条
  • [1] Improving Risk Stratification of SCAI Stages of Cardiogenic Shock With Machine Learning-Based Phenotyping
    Zweck, Elric
    Kanwar, Manreet
    Li, Song
    Sinha, Shashank
    Garan, Arthur
    Montfort, Jaime Hernandez
    Abraham, Jacob
    Polzin, Amin
    Kelm, Malte
    Burkhoff, Daniel
    Kapur, Navin
    JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2024, 84 (18) : B50 - B51
  • [2] Machine Learning-Based Clinical Predictive Models for Early Readmission in Patients with Cardiogenic Shock
    Tieliwaerdi, Xiarepati
    Abuduweili, Abulikemu
    Mutabi, Erasmus
    Manalo, Kathryn
    Lander, Matthew
    CIRCULATION, 2024, 150
  • [3] Machine learning-based prediction of mortality in acute myocardial infarction with cardiogenic shock
    Zhang, Qitian
    Xu, Lizhen
    Xie, Zhiyi
    He, Weibin
    Huang, Xiaohong
    FRONTIERS IN CARDIOVASCULAR MEDICINE, 2024, 11
  • [4] Machine learning-based scoring system to predict cardiogenic shock in acute coronary syndrome
    Bohm, Allan
    Segev, Amitai
    Jajcay, Nikola
    Krychtiuk, Konstantin A.
    Tavazzi, Guido
    Spartalis, Michael
    Kollarova, Marta
    Berta, Imrich
    Jankova, Jana
    Guerra, Frederico
    Pogran, Edita
    Remak, Andrej
    Jarakovic, Milana
    Sebenova Jerigova, Viera
    Petrikova, Katarina
    Matetzky, Shlomi
    Skurk, Carsten
    Huber, Kurt
    Bezak, Branislav
    EUROPEAN HEART JOURNAL - DIGITAL HEALTH, 2025, 6 (02): : 240 - 251
  • [5] Phenotyping Refractory Cardiogenic Shock Patients Receiving Venous-Arterial Extracorporeal Membrane Oxygenation Using Machine Learning Algorithms
    Wang, Shuo
    Wang, Liangshan
    Du, Zhongtao
    Yang, Feng
    Hao, Xing
    Wang, Xiaomeng
    Shao, Chengcheng
    Li, Jin
    Wang, Hong
    Li, Chenglong
    Hou, Xiaotong
    REVIEWS IN CARDIOVASCULAR MEDICINE, 2024, 25 (08)
  • [6] Machine learning-based automated phenotyping of inflammatory nocifensive behavior in mice
    Wotton, Janine M.
    Peterson, Emma
    Anderson, Laura
    Murray, Stephen A.
    Braun, Robert E.
    Chesler, Elissa J.
    White, Jacqueline K.
    Kumar, Vivek
    MOLECULAR PAIN, 2020, 16
  • [7] A Machine Learning-Based Evaluation Method for Machine Translation
    Kotani, Katsunori
    Yoshimi, Takehiko
    ARTIFICIAL INTELLIGENCE: THEORIES, MODELS AND APPLICATIONS, PROCEEDINGS, 2010, 6040 : 351 - +
  • [8] MACHINE LEARNING FOR EARLY PREDICTION OF CARDIOGENIC SHOCK
    Chang, Yale
    Antonescu, Corneliu
    Ravindranath, Shreyas Raj
    Dong, Junzi
    Lu, MingYu
    Vicario, Francesco
    Wondrely, Lisa
    Thompson, Pam
    Swearingen, Dennis
    Acharya, Deepak
    JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2022, 79 (09) : 278 - 278
  • [9] Machine Learning for Prediction of Outcomes in Cardiogenic Shock
    Rong, Fangning
    Xiang, Huaqiang
    Qian, Lu
    Xue, Yangjing
    Ji, Kangting
    Yin, Ripen
    FRONTIERS IN CARDIOVASCULAR MEDICINE, 2022, 9
  • [10] Evaluation of machine learning-based solutions for health
    Antoniou, Tony
    Mamdani, Muhammad
    CANADIAN MEDICAL ASSOCIATION JOURNAL, 2021, 193 (44) : E1720 - E1724
12345