Original language | English (US) |
---|---|
Pages (from-to) | 287-300 |
Number of pages | 14 |
Journal | Heart Failure Clinics |
Volume | 18 |
Issue number | 2 |
DOIs | |
State | Published - Apr 2022 |
Keywords
- Artificial intelligence
- Deep learning
- Heart failure
- Machine learning
- Natural language processing
ASJC Scopus subject areas
- Cardiology and Cardiovascular Medicine
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In: Heart Failure Clinics, Vol. 18, No. 2, 04.2022, p. 287-300.
Research output: Contribution to journal › Review article › peer-review
TY - JOUR
T1 - Advances in Machine Learning Approaches to Heart Failure with Preserved Ejection Fraction
AU - Ahmad, Faraz S.
AU - Luo, Yuan
AU - Wehbe, Ramsey M.
AU - Thomas, James D.
AU - Shah, Sanjiv J.
N1 - Funding Information: Dr Ahmad was supported by grants from the Agency for Healthcare Research and Quality (K12HS026385), National Institutes of Health /National Heart, Lung , and Blood Institute (K23HL155970), and the American Heart Association (AHA number 856917). Dr Luo was supported by grants from National Institutes of Health (U01TR003528, 1R01LM013337). Dr Thomas was supported by a grant from the Irene D. Pritzker Foundation . The statements presented in this work are solely the responsibility of the author(s) and do not necessarily represent the official views of the Patient-Centered Outcomes Research Institute (PCORI), the PCORI Board of Governors or Methodology Committee, the Agency for Healthcare Research and Quality, the National Institutes of Health, or the American Heart Association. Funding Information: F.A. receives consulting fees from Amgen, Pfizer, and Livongo Teladoc outside of this work. S.J.S. has received research grants from the National Institutes of Health (R01 HL107577, R01 HL127028, R01 HL140731, R01 HL149423), Actelion , AstraZeneca , Corvia , Novartis , and Pfizer and has received consulting fees from Abbott, Actelion, AstraZeneca, Amgen, Aria CV, Axon Therapies, Bayer, Boehringer-Ingelheim, Boston Scientific, Bristol-Myers Squibb, Cardiora, CVRx, Cytokinetics, Edwards Lifesciences, Eidos, Eisai, Imara, Impulse Dynamics, Intellia, Ionis, Ironwood, Lilly, Merck, MyoKardia, Novartis, Novo Nordisk, Pfizer, Prothena, Regeneron, Rivus, Sanofi, Shifamed, Tenax, Tenaya, and United Therapeutics. J.T. receives consulting fees from Edwards, Abbott, GE, and Caption Health and reports spouse employment with Caption Health. Funding Information: F.A. receives consulting fees from Amgen, Pfizer, and Livongo Teladoc outside of this work. S.J.S. has received research grants from the National Institutes of Health (R01 HL107577, R01 HL127028, R01 HL140731, R01 HL149423), Actelion, AstraZeneca, Corvia, Novartis, and Pfizer and has received consulting fees from Abbott, Actelion, AstraZeneca, Amgen, Aria CV, Axon Therapies, Bayer, Boehringer-Ingelheim, Boston Scientific, Bristol-Myers Squibb, Cardiora, CVRx, Cytokinetics, Edwards Lifesciences, Eidos, Eisai, Imara, Impulse Dynamics, Intellia, Ionis, Ironwood, Lilly, Merck, MyoKardia, Novartis, Novo Nordisk, Pfizer, Prothena, Regeneron, Rivus, Sanofi, Shifamed, Tenax, Tenaya, and United Therapeutics. J.T. receives consulting fees from Edwards, Abbott, GE, and Caption Health and reports spouse employment with Caption Health.Dr Ahmad was supported by grants from the Agency for Healthcare Research and Quality (K12HS026385), National Institutes of Health/National Heart, Lung, and Blood Institute (K23HL155970), and the American Heart Association (AHA number 856917). Dr Luo was supported by grants from National Institutes of Health (U01TR003528, 1R01LM013337). Dr Thomas was supported by a grant from the Irene D. Pritzker Foundation. The statements presented in this work are solely the responsibility of the author(s) and do not necessarily represent the official views of the Patient-Centered Outcomes Research Institute (PCORI), the PCORI Board of Governors or Methodology Committee, the Agency for Healthcare Research and Quality, the National Institutes of Health, or the American Heart Association.
PY - 2022/4
Y1 - 2022/4
KW - Artificial intelligence
KW - Deep learning
KW - Heart failure
KW - Machine learning
KW - Natural language processing
UR - http://www.scopus.com/inward/record.url?scp=85125705723&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125705723&partnerID=8YFLogxK
U2 - 10.1016/j.hfc.2021.12.002
DO - 10.1016/j.hfc.2021.12.002
M3 - Review article
C2 - 35341541
AN - SCOPUS:85125705723
SN - 1551-7136
VL - 18
SP - 287
EP - 300
JO - Heart Failure Clinics
JF - Heart Failure Clinics
IS - 2
ER -