Defining "Better Prediction" by Machine-Learning Models Toward Clinical Application

被引：0
|
作者：
Hamaya, Rikuta [1 ]
Sahashi, Yuki [1 ]
Kagiyama, Nobuyuki [1 ]
机构：
[1] Brigham & Womens Hosp, Div Prevent Med, 900 Commonwealth Ave, Boston, MA 02115 USA
来源：
JACC-CARDIOVASCULAR IMAGING | 2022年 / 15卷 / 03期
关键词：
D O I：
10.1016/j.jcmg.2021.03.033
中图分类号：
R5 [内科学];
学科分类号：
1002 ; 100201 ;
摘要：
引用
收藏
页码：550 / 550
页数：1
相关论文
共 50 条
  • [1] Optimizing machine-learning models for mutagenicity prediction through better feature selection
    Shinada, Nicolas K.
    Koyama, Naoki
    Ikemori, Megumi
    Nishioka, Tomoki
    Hitaoka, Seiji
    Hakura, Atsushi
    Asakura, Shoji
    Matsuoka, Yukiko
    Palaniappan, Sucheendra K.
    [J]. MUTAGENESIS, 2022, 37 (3-4) : 191 - 202
  • [2] Advancing interpretability of machine-learning prediction models
    Trenary, Laurie
    DelSole, Timothy
    [J]. ENVIRONMENTAL DATA SCIENCE, 2022, 1
  • [3] An investigation on machine-learning models for the prediction of cyanobacteria growth
    Giere, Johannes
    Riley, Derek
    Nowling, R. J.
    McComack, Joshua
    Sander, Hedda
    [J]. FUNDAMENTAL AND APPLIED LIMNOLOGY, 2020, 194 (02) : 85 - 94
  • [4] Machine-learning models for prediction of sepsis patients mortality
    Bao, C.
    Deng, F.
    Zhao, S.
    [J]. MEDICINA INTENSIVA, 2023, 47 (06) : 315 - 325
  • [5] Toward a unified framework for interpreting machine-learning models in neuroimaging
    Kohoutova, Lada
    Heo, Juyeon
    Cha, Sungmin
    Lee, Sungwoo
    Moon, Taesup
    Wager, Tor D.
    Woo, Choong-Wan
    [J]. NATURE PROTOCOLS, 2020, 15 (04) : 1399 - 1435
  • [6] Toward a unified framework for interpreting machine-learning models in neuroimaging
    Lada Kohoutová
    Juyeon Heo
    Sungmin Cha
    Sungwoo Lee
    Taesup Moon
    Tor D. Wager
    Choong-Wan Woo
    [J]. Nature Protocols, 2020, 15 : 1399 - 1435
  • [7] Certified Machine-Learning Models
    Damiani, Ernesto
    Ardagna, Claudio A.
    [J]. SOFSEM 2020: THEORY AND PRACTICE OF COMPUTER SCIENCE, 2020, 12011 : 3 - 15
  • [8] Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters
    Stork, Conrad
    Chen, Ya
    Sicho, Martin
    Kirchmair, Johannes
    [J]. JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2019, 59 (03) : 1030 - 1043
  • [9] Machine-learning models for prediction of sepsis patients mortality: A needed consideration
    Fernandez, Marcos Valiente
    Moya, Francisco de Paula Delgado
    de Aledo, Amanda Lesmes Gonzalez
    Badia, Isaias Martin
    [J]. MEDICINA INTENSIVA, 2023, 47 (07) : 416 - 417
  • [10] Ensemble Machine-Learning Models for Accurate Prediction of Solar Irradiation in Bangladesh
    Alam, Md Shafiul
    Al-Ismail, Fahad Saleh
    Hossain, Md Sarowar
    Rahman, Syed Masiur
    [J]. PROCESSES, 2023, 11 (03)
12345