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

Faraz S. Ahmad, Yuan Luo, Ramsey M. Wehbe, James D. Thomas, Sanjiv J. Shah*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

11 Scopus citations
Original languageEnglish (US)
Pages (from-to)287-300
Number of pages14
JournalHeart Failure Clinics
Volume18
Issue number2
DOIs
StatePublished - Apr 2022

Funding

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. 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.

Keywords

  • Artificial intelligence
  • Deep learning
  • Heart failure
  • Machine learning
  • Natural language processing

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

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