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

共 75 条

[21] Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges [J].

Goldstein, Benjamin A. ;

Navar, Ann Marie ;

Carter, Rickey E. .

EUROPEAN HEART JOURNAL, 2017, 38 (23) :1805-1814

[22]

Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1

[23] Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms [J].

Goto, Shinichi ;

Mahara, Keitaro ;

Beussink-Nelson, Lauren ;

Ikura, Hidehiko ;

Katsumata, Yoshinori ;

Endo, Jin ;

Gaggin, Hanna K. ;

Shah, Sanjiv J. ;

Itabashi, Yuji ;

MacRae, Calum A. ;

Deo, Rahul C. .

NATURE COMMUNICATIONS, 2021, 12 (01)

[24] A machine learning risk score predicts mortality across the spectrum of left ventricular ejection fraction [J].

Greenberg, Barry ;

Adler, Eric ;

Campagnari, Claudio ;

Yagil, Avi .

EUROPEAN JOURNAL OF HEART FAILURE, 2021, 23 (06) :995-999

[25] Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine [J].

Guo, Lin Lawrence ;

Pfohl, Stephen R. ;

Fries, Jason ;

Posada, Jose ;

Fleming, Scott Lanyon ;

Aftandilian, Catherine ;

Shah, Nigam ;

Sung, Lillian .

APPLIED CLINICAL INFORMATICS, 2021, 12 (04) :808-815

[26]

Hartigan J. A., 1979, Applied Statistics, V28, P100, DOI 10.2307/2346830

[27] Caveats for the Use of Operational Electronic Health Record Data in Comparative Effectiveness Research [J].

Hersh, William R. ;

Weiner, Mark G. ;

Embi, Peter J. ;

Logan, Judith R. ;

Payne, Philip R. O. ;

Bernstam, Elmer V. ;

Lehmann, Harold P. ;

Hripcsak, George ;

Hartzog, Timothy H. ;

Cimino, James J. ;

Saltz, Joel H. .

MEDICAL CARE, 2013, 51 (08) :S30-S37

[28] Advances in natural language processing [J].

Hirschberg, Julia ;

Manning, Christopher D. .

SCIENCE, 2015, 349 (6245) :261-266

[29] Limestone: High-throughput candidate phenotype generation via tensor factorization [J].

Ho, Joyce C. ;

Ghosh, Joydeep ;

Steinhubl, Steve R. ;

Stewart, Walter F. ;

Denny, Joshua C. ;

Malin, Bradley A. ;

Sun, Jimeng .

JOURNAL OF BIOMEDICAL INFORMATICS, 2014, 52 :199-211

[30] A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy [J].

Huda, Ahsan ;

Castano, Adam ;

Niyogi, Anindita ;

Schumacher, Jennifer ;

Stewart, Michelle ;

Bruno, Marianna ;

Hu, Mo ;

Ahmad, Faraz S. ;

Deo, Rahul C. ;

Shah, Sanjiv J. .

NATURE COMMUNICATIONS, 2021, 12 (01)

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