Machine Learning Analysis of Identifies Polyunsaturated Fatty Acid Metabolites Predictive of Adverse Outcomes In Heart Failure with Preserved Ejection Fraction Patients

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DOI:

https://doi.org/10.46570/utjms.vol11-2023-951

Keywords:

Machine learning, Polyunsaturated Fatty Acid (PUFA) metabolites, Heart failure with preserved ejection fraction (HFpEF), Cardiovascular health, Lipidomics framework
Journal Cover Art for Volume 11, Issue 3

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Published

2023-12-15

How to Cite

Faleel, D., Elzanaty, A., Aradhyula, V., Vyas, R., Dube, P., T. Haller, S., Gupta, R., J. Kennedy, D., & J. Khouri, S. (2023). Machine Learning Analysis of Identifies Polyunsaturated Fatty Acid Metabolites Predictive of Adverse Outcomes In Heart Failure with Preserved Ejection Fraction Patients . Translation: The University of Toledo Journal of Medical Sciences, 11(3). https://doi.org/10.46570/utjms.vol11-2023-951

Issue

Vol. 11 No. 3 (2023): Dr. Lance D. Dworkin Department of Medicine Research Symposium

Section

Dr. Lance D. Dworkin Department of Medicine Research Symposium

License

Copyright (c) 2023 Dhilhani Faleel, Ahmed Elzanaty, Vaishnavi Aradhyula, Rohit Vyas, Prabhatchandra Dube, Steven T. Haller, Rajesh Gupta, David J. Kennedy, Samer J. Khouri

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