PROGNOSTIC IMPLICATIONS OF MACHINE LEARNING-DERIVED ECHOCARDIOGRAPHIC PHENOTYPES IN ASYMPTOMATIC COMMUNITY HYPERTENSIVE POPULATIONS

被引:0
|
作者
Cai, Anping [1 ]
Zhou, Dan [1 ]
Zhou, Yingling [1 ]
Tang, Songtao [1 ]
Feng, Yingqing [1 ]
Nie, Zhiqiang [1 ]
机构
[1] Guangdong Prov Peoples Hosp, Guangzhou, Peoples R China
关键词
D O I
暂无
中图分类号
R6 [外科学];
学科分类号
1002 ; 100210 ;
摘要
PS-BPC01-5
引用
收藏
页码:E292 / E292
页数:1
相关论文
共 50 条
  • [21] Diagnostic Performance of Machine Learning-Derived OSA Prediction Tools in Large Clinical and Community-Based Samples
    Holfinger, Steven J.
    Lyons, M. Melanie
    Keenan, Brendan T.
    Mazzotti, Diego R.
    Mindel, Jesse
    Maislin, Greg
    Cistulli, Peter A.
    Sutherland, Kate
    McArdle, Nigel
    Singh, Bhajan
    Chen, Ning-Hung
    Gislason, Thorarinn
    Penzel, Thomas
    Han, Fang
    Li, Qing Yun
    Schwab, Richard
    Pack, Allan I.
    Magalang, Ulysses J.
    CHEST, 2022, 161 (03) : 807 - 817
  • [22] Validation of machine learning-derived heart failure phenotypes from the United States PINNACLE registry in the Swedish heart failure registry
    Sarajlic, P.
    Abboud, A.
    Kirshenbaum, D.
    Song, Y.
    Hage, C.
    Shah, S. J.
    Lund, L. H.
    Back, M.
    Gao, Q.
    Lee, J. J.
    Savarese, G.
    Doros, G.
    Januzzi, J. L.
    Dahlstrom, U.
    Gaggin, H. K.
    EUROPEAN JOURNAL OF HEART FAILURE, 2021, 23 : 78 - 79
  • [23] Machine Learning-Derived Personalized Fluid Intake Strategy in SepsisAssociated AKI
    Oh, Wonsuk
    Takkavatakarn, Kullaya
    Kohli-Seth, Roopa D.
    Nadkarni, Girish N.
    Sakhuja, Ankit
    JOURNAL OF THE AMERICAN SOCIETY OF NEPHROLOGY, 2024, 35 (10):
  • [24] Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes
    Julian Libiseller-Egger
    Jody E. Phelan
    Zachi I. Attia
    Ernest Diez Benavente
    Susana Campino
    Paul A. Friedman
    Francisco Lopez-Jimenez
    David A. Leon
    Taane G. Clark
    Scientific Reports, 12
  • [25] Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes
    Libiseller-Egger, Julian
    Phelan, Jody E. E.
    Attia, Zachi I. I.
    Benavente, Ernest Diez
    Campino, Susana
    Friedman, Paul A. A.
    Lopez-Jimenez, Francisco
    Leon, David A. A.
    Clark, Taane G. G.
    SCIENTIFIC REPORTS, 2022, 12 (01)
  • [26] A Supervised Machine Learning-Derived Mortality/Stroke Risk Calculator: A Prognostic Score in the Modern Era of Carotid Artery Revascularization
    Willie-permor, Daniel
    Rahgozar, Shima
    Zarrintan, Sina
    Gaffey, Ann C.
    Malas, Mahmoud
    JOURNAL OF THE AMERICAN COLLEGE OF SURGEONS, 2023, 237 (05) : S584 - S585
  • [27] A Novel Application of Unsupervised Machine Learning and Supervised Machine Learning-Derived Radiomics in Anterior Cruciate Ligament Rupture
    Chen, De-Sheng
    Wang, Tong-Fu
    Zhu, Jia-Wang
    Zhu, Bo
    Wang, Zeng-Liang
    Cao, Jian-Gang
    Feng, Cai-Hong
    Zhao, Jun-Wei
    RISK MANAGEMENT AND HEALTHCARE POLICY, 2021, 14 : 2657 - 2664
  • [28] Congenital Heart Surgery Machine Learning-Derived In-Depth Benchmarking Tool
    Sarris, George E.
    Zhuo, Daisy
    Mingardi, Luca
    Dunn, Jack
    Levine, Jordan
    Tobota, Zdzislaw
    Maruszewski, Bohdan
    Fragata, Jose
    Bertsimas, Dimitris
    ANNALS OF THORACIC SURGERY, 2024, 118 (01): : 199 - 206
  • [29] Reducing Intraoperative Hypotension Using a Machine Learning-Derived Early Warning System
    de Tymowski, Christian
    Longrois, Dan
    Montravers, Philippe
    JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2020, 324 (08): : 806 - 807
  • [30] Classification of Plant Endogenous States Using Machine Learning-Derived Agricultural Indices
    Koh, Sally Shuxian
    Dev, Kapil
    Tan, Javier Jingheng
    Teo, Valerie Xinhui
    Zhang, Shuyan
    Dinish, U. S.
    Olivo, Malini
    Urano, Daisuke
    PLANT PHENOMICS, 2023, 5