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Comparison among random forest, logistic regression, and existing clinical risk scores for predicting outcomes in patients with atrial fibrillation: A report from the J-RHYTHM registry
Clinical Cardiology ( IF 2.7 ) Pub Date : 2021-07-28 , DOI: 10.1002/clc.23688 Eiichi Watanabe 1 , Shunsuke Noyama 2 , Ken Kiyono 2 , Hiroshi Inoue 3 , Hirotsugu Atarashi 4 , Ken Okumura 5 , Takeshi Yamashita 6 , Gregory Y H Lip 7 , Eitaro Kodani 8 , Hideki Origasa 9
Clinical Cardiology ( IF 2.7 ) Pub Date : 2021-07-28 , DOI: 10.1002/clc.23688 Eiichi Watanabe 1 , Shunsuke Noyama 2 , Ken Kiyono 2 , Hiroshi Inoue 3 , Hirotsugu Atarashi 4 , Ken Okumura 5 , Takeshi Yamashita 6 , Gregory Y H Lip 7 , Eitaro Kodani 8 , Hideki Origasa 9
Affiliation
Machine learning (ML) has emerged as a promising tool for risk stratification. However, few studies have applied ML to risk assessment of patients with atrial fibrillation (AF).
中文翻译：
随机森林、逻辑回归和现有临床风险评分预测房颤患者预后的比较：来自 J-RHYTHM 注册中心的报告
机器学习 (ML) 已成为一种很有前途的风险分层工具。然而，很少有研究将 ML 应用于房颤 (AF) 患者的风险评估。
更新日期：2021-09-09
中文翻译：
随机森林、逻辑回归和现有临床风险评分预测房颤患者预后的比较：来自 J-RHYTHM 注册中心的报告
机器学习 (ML) 已成为一种很有前途的风险分层工具。然而，很少有研究将 ML 应用于房颤 (AF) 患者的风险评估。
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