Advances in Machine Learning Approaches to Heart Failure with Preserved Ejection Fraction
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Heart failure with preserved ejection fraction (HFpEF) represents a prototypical cardiovascular condition in which machine learning may improve targeted therapies and mechanistic understanding of pathogenesis. Machine learning, which involves algorithms that learn from data, has the potential to guide precision medicine approaches for complex clinical syndromes such as HFpEF. It is therefore important to understand the potential utility and common pitfalls of machine learning so that it can be applied and interpreted appropriately. Although machine learning holds considerable promise for HFpEF, it is subject to several potential pitfalls, which are important factors to consider when interpreting machine learning studies.
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Data fusion efforts are expected to result in an improvement in predictive power and therefore provide more reliable results in potentially low validity settings.
“The expectation in machine learning is that data fusion efforts will result in an improvement in predictive power17,18 and therefore provide more reliable results in potentially low validity settings19.”
From Multimodal machine learning in precision health: A scoping review (2022) · cites this paper as [18]
The cited paper's Discussion says ML cannot overcome threats to validity in small-subgroup or post-hoc analyses, and that such findings must be treated cautiously. That contradicts the claim attributed to citation 19 that data fusion would provide more reliable results in potentially low-validity settings. The paper does discuss the potential for improved prediction using diverse data sources, but does not extend that promise to overcoming validity problems. (LLM, conf=0.88)
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Deep learning for multisource medical information processing
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From patterns to prognosis: machine learning–derived clusters in advanced heart failure
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Applying Natural Language Processing to Electronic Health Record Data—From Text to Triage
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Machine Learning Approaches for Predicting Heart Failure from Echocardiography Data
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Artificial Intelligence in Heart Failure with Preserved Ejection Fraction
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- Authors
- Ahmad, F. S., Luo, Y., Wehbe, R. M., Thomas, J. D., & Shah, S. J.
- Affiliations
- Northwestern University (US)
- Corresponding author
- Shah, Sanjiv J.
- Venue
- Heart Failure Clinics
- Publisher
- Elsevier BV
- Published
- 2022-04
- Volume
- 18
- Issue
- 2
- Pages
- 287-300
- ISSN
- 1551-7136 (ISSN-L)
- PMC
- PMC8983114
- OpenAlex
- W4214942785
- Cited by
- 34
- References
- 75
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 8 · 2023: 7 · 2022: 2
- FWCI
- 1.02
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 357 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Heart Failure Clinics (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Pfizer
- Patient-Centered Outcomes Research Institute
- National Institutes of Health (R01 HL107577, R01 HL127028, R01 HL140731, R01 HL149423)
- Edwards Lifesciences
- National Heart, Lung, and Blood Institute (K23HL155970)
- United Therapeutics Corporation
- Agency for Healthcare Research and Quality (K12HS026385)
- Cytokinetics
- American Heart Association (1R01LM013337, 856917, U01TR003528)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Failure Treatment and Management
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Function and Risk Factors
- Keywords
- heart failure with preserved ejection fraction, machine learning, precision medicine, targeted therapy
- MeSH terms
- Machine Learning, Heart Failure, Humans, Stroke Volume, Ventricular Function, Left, Precision Medicine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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