ReviewJournal of cardiovascular translational research2017
Tensor Factorization for Precision Medicine in Heart Failure with Preserved Ejection Fraction.
Review in Journal of cardiovascular translational research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed.
- Labour monitoring and decision support: a machine-learning-based paradigm.Frontiers in global women's health · 2025Article
- Impact of tooth loss and patient characteristics on coronary artery calcium score classification and prediction.Scientific reports · 2024Article
- Censored Least Squares for Imputing Missing Values in PARAFAC Tensor Factorization.bioRxiv : the preprint server for biology · 2024Article
- A New Fuzzy-Based Classification Method for Use in Smart/Precision Medicine.Bioengineering (Basel, Switzerland) · 2023Article
- A cardiologist's guide to machine learning in cardiovascular disease prognosis prediction.Basic research in cardiology · 2023Review
- A deep learning framework assisted echocardiography with diagnosis, lesion localization, phenogrouping heterogeneous disease, and anomaly detection.Scientific reports · 2023Article
- Computational clustering reveals differentiated coronary artery calcium progression at prevalent levels of pulse wave velocity by classifying high-risk patients.Frontiers in cardiovascular medicine · 2023Article
- Multimodal machine learning in precision health: A scoping review.NPJ digital medicine · 2022Article
- Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis.Human genomics · 2021Article
- Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review.Journal of the American College of Cardiology · 2021Review
- Big Data, Extracting Insights, Comprehension, and Analytics in Cardiology: An Overview.Journal of healthcare engineering · 2021Review
- Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis.Human genomics · 2020Article
- Identifying Phenogroups in patients with subclinical diastolic dysfunction using unsupervised statistical learning.BMC cardiovascular disorders · 2020Article
- Artificial intelligence supported patient self-care in chronic heart failure: a paradigm shift from reactive to predictive, preventive and personalised care.The EPMA journal · 2019Review
- Metabolomics technology and bioinformatics for precision medicine.Briefings in bioinformatics · 2019Review
- Cancer classification and pathway discovery using non-negative matrix factorization.Journal of biomedical informatics · 2019Article
- Supervised Nonnegative Matrix Factorization to Predict ICU Mortality Risk.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2018Article
- Big Data and Data Science in Critical Care.Chest · 2018Review
- Phenomapping for the Identification of Hypertensive Patients with the Myocardial Substrate for Heart Failure with Preserved Ejection Fraction.Journal of cardiovascular translational research · 2017Article
- Precision Medicine for Heart Failure with Preserved Ejection Fraction: An Overview.Journal of cardiovascular translational research · 2017Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome that may benefit from improved subtyping in order to better characterize its pathophysiology and to develop novel targeted therapies. The United States Precision Medicine Initiative comes amid the rapid growth in quantity and modality of clinical data for HFpEF patients ranging from deep phenotypic to trans-omic data. Tensor factorization, a form of machine learning, allows for the integration of multiple data modalities to derive clinically relevant HFpEF subtypes that may have significant differences in underlying pathophysiology and differential response to therapies. Tensor factorization also allows for better interpretability by supporting dimensionality reduction and identifying latent groups of data for meaningful summarization of both features and disease outcomes. In this narrative review, we analyze the modest literature on the application of tensor factorization to related biomedical fields including genotyping and phenotyping. Based on the cited work including work of our own, we suggest multiple tensor factorization formulations capable of integrating the deep phenotypic and trans-omic modalities of data for HFpEF, or accounting for interactions between genetic variants at different omic hierarchies. We encourage extensive experimental studies to tackle challenges in applying tensor factorization for precision medicine in HFpEF, including effectively incorporating existing medical knowledge, properly accounting for uncertainty, and efficiently enforcing sparsity for better interpretability.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.