Advances in Machine Learning Approaches to Heart Failure with Preserved Ejection Fraction

被引：18

作者：

Ahmad, Faraz S. ^{[1
,2
,3
,5
]}

Luo, Yuan ^{[2
,3
,6
]}

Wehbe, Ramsey M. ^{[1
,3
,5
]}

Thomas, James D. ^{[1
,3
,5
]}

Shah, Sanjiv J. ^{[1
,3
,4
,5
]}

机构：

[1] Northwestern Univ, Dept Med, Feinberg Sch Med, Div Cardiol, Chicago, IL 60611 USA

[2] Northwestern Univ, Dept Prevent Med, Feinberg Sch Med, Chicago, IL 60611 USA

[3] Northwestern Med, Ctr Artificial Intelligence, Bluhm Cardiovasc Inst, Chicago, IL USA

[4] Northwestern Univ, Feinberg Sch Med, Ctr Deep Phenotyping & Precis Therapeut, Inst Augmented Intelligence Med, 676 North St Clair St,Suite 730, Chicago, IL 60611 USA

[5] 676 North St Clair St,Suite 600, Chicago, IL 60611 USA

[6] Rubloff Bldg,11th Floor,750 North Lake Shore, Chicago, IL 60611 USA

来源：

HEART FAILURE CLINICS | 2022年 / 18卷 / 02期

基金：

美国医疗保健研究与质量局; 美国国家卫生研究院;

关键词：

Heart failure; Artificial intelligence; Machine learning; Deep learning; Natural language processing; DATASET SHIFT; HEALTH-CARE; FUTURE; STATE; TEXT;

D O I：

10.1016/j.hfc.2021.12.002

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

center dot Machine learning has the potential to guide precision medicine approaches for heart failure with preserved ejection fraction, such as identification of rare causes, subphenotyping, and increasing the efficiency of clinical trial enrollment. center dot Understanding the strengths, limitations, and pitfalls of machine learning approaches is critical to realizing the potential of machine learning to impact the health of the patient with heart failure with preserved ejection fraction.

引用

页码：287 / 300

页数：14

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