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Published in: Current Heart Failure Reports 5/2020

Open Access 01-10-2020 | Heart Failure | Translational Research in Heart Failure (J. Backs and M. van den Hoogenhof, Section Editors)

Big Data Approaches in Heart Failure Research

Authors: Jan D. Lanzer, Florian Leuschner, Rafael Kramann, Rebecca T. Levinson, Julio Saez-Rodriguez

Published in: Current Heart Failure Reports | Issue 5/2020

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Abstract

Purpose of Review

The goal of this review is to summarize the state of big data analyses in the study of heart failure (HF). We discuss the use of big data in the HF space, focusing on “omics” and clinical data. We address some limitations of this data, as well as their future potential.

Recent Findings

Omics are providing insight into plasmal and myocardial molecular profiles in HF patients. The introduction of single cell and spatial technologies is a major advance that will reshape our understanding of cell heterogeneity and function as well as tissue architecture. Clinical data analysis focuses on HF phenotyping and prognostic modeling.

Summary

Big data approaches are increasingly common in HF research. The use of methods designed for big data, such as machine learning, may help elucidate the biology underlying HF. However, important challenges remain in the translation of this knowledge into improvements in clinical care.
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Metadata
Title
Big Data Approaches in Heart Failure Research
Authors
Jan D. Lanzer
Florian Leuschner
Rafael Kramann
Rebecca T. Levinson
Julio Saez-Rodriguez
Publication date
01-10-2020
Publisher
Springer US
Keyword
Heart Failure
Published in
Current Heart Failure Reports / Issue 5/2020
Print ISSN: 1546-9530
Electronic ISSN: 1546-9549
DOI
https://doi.org/10.1007/s11897-020-00469-9

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