Machine Learning To Predict High Risk Adverse Events In Treatment Trials For Intracerebral Hemorrhage

被引:0
|
作者
Tariq, Muhammad Bilal
Ling, Yaobin
Savitz, Sean I.
Fann, Yang C.
Jiang, Xiaoqian
Kim, Yejin
机构
关键词
D O I
10.1161/str.54.suppl_1.WP123
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
AWP123
引用
收藏
页数:3
相关论文
共 50 条
  • [1] Machine learning for adverse events: PREDICT
    Sassack, Kali
    McDade, Elizabeth
    Runtas, Cali
    Grebe, Lori
    CURRENT MEDICAL RESEARCH AND OPINION, 2022, 38 : 5 - 5
  • [2] Risk of thromboembolic events in controlled trials of rFVIIa in spontaneous intracerebral hemorrhage
    Diringer, Michael N.
    Skolnick, Brett E.
    Mayer, Stephan A.
    Steiner, Thorsten
    Davis, Stephen M.
    Brun, Nikolai C.
    Broderick, Joseph P.
    STROKE, 2008, 39 (03) : 850 - 856
  • [3] Using Machine Learning to Predict Tracheostomy After Intracerebral Hemorrhage.
    Garg, Ravi
    Prabhakaran, Shyam
    Holl, Jane
    Faigle, Roland
    Naidech, Andrew
    STROKE, 2019, 50
  • [4] Machine learning models predict coagulopathy in spontaneous intracerebral hemorrhage patients in ER
    Zhu, Fengping
    Pan, Zhiguang
    Tang, Ying
    Fu, Pengfei
    Cheng, Sijie
    Hou, Wenzhong
    Zhang, Qi
    Huang, Hong
    Sun, Yirui
    CNS NEUROSCIENCE & THERAPEUTICS, 2021, 27 (01) : 92 - 100
  • [5] Improving the Accuracy of Scores to Predict Gastrostomy after Intracerebral Hemorrhage with Machine Learning
    Garg, Ravi
    Prabhakaran, Shyam
    Holl, Jane L.
    Luo, Yuan
    Faigle, Roland
    Kording, Konrad
    Naidech, Andrew M.
    JOURNAL OF STROKE & CEREBROVASCULAR DISEASES, 2018, 27 (12): : 3570 - 3574
  • [6] Association of Prior Intracerebral Hemorrhage With Major Adverse Cardiovascular Events
    Gaist, David
    Hald, Stine Munk
    Garcia Rodriguez, Luis Alberto
    Clausen, Anne
    Moller, Soren
    Hallas, Jesper
    Salman, Rustam Al-Shahi
    JAMA NETWORK OPEN, 2022, 5 (10) : E2234215
  • [7] Novel machine learning models to predict pneumonia events in supratentorial intracerebral hemorrhage populations: An analysis of the Risa-MIS-ICH study
    Zheng, Yan
    Lin, Yuan-Xiang
    He, Qiu
    Zhuo, Ling-Yun
    Huang, Wei
    Gao, Zhu-Yu
    Chen, Ren-Long
    Zhao, Ming-Pei
    Xie, Ze-Feng
    Ma, Ke
    Fang, Wen-Hua
    Wang, Deng-Liang
    Chen, Jian-Cai
    Kang, De-Zhi
    Lin, Fu-Xin
    FRONTIERS IN NEUROLOGY, 2022, 13
  • [8] Machine Learning Approaches to Predict Major Adverse Cardiovascular Events in Atrial Fibrillation
    Molto-Balado, Pedro
    Reverte-Villarroya, Silvia
    Alonso-Barberan, Victor
    Monclus-Arasa, Cinta
    Balado-Albiol, Maria Teresa
    Clua-Queralt, Josep
    Clua-Espuny, Josep-Lluis
    TECHNOLOGIES, 2024, 12 (02)
  • [9] Machine learning algorithms to predict major adverse cardiovascular events in patients with diabetes
    Abegaz, Tadesse M.
    Baljoon, Ahmead
    Kilanko, Oluwaseun
    Sherbeny, Fatimah
    Ali, Askal Ayalew
    COMPUTERS IN BIOLOGY AND MEDICINE, 2023, 164
  • [10] Risk of Arterial Thromboembolic Events After Intracerebral Hemorrhage
    Murthy, Santosh
    Merkler, Alexander
    Gialdini, Gino
    Iadecola, Costantino
    Navi, Babak
    Kamel, Hooman
    STROKE, 2017, 48