A Random Shuffle Method to Expand a Narrow Dataset and Overcome the Associated Challenges in a Clinical Study: A Heart Failure Cohort Example

A Random Shuffle Method to Expand a Narrow Dataset and Overcome the Associated Challenges in a Clinical Study: A Heart Failure Cohort Example

Heart failure (HF) affects at least 26 million people worldwide, so predicting adverse events in HF patients represents a major target of clinical data science. However, achieving large sample sizes sometimes represents a challenge due to difficulties in patient recruiting and long follow-up times,...

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Bibliographic Details
Main Authors: Lorenzo Fassina, Alessandro Faragli, Francesco Paolo Lo Muzio, Sebastian Kelle, Carlo Campana, Burkert Pieske, Frank Edelmann, Alessio Alogna
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-11-01
Series:Frontiers in Cardiovascular Medicine
Subjects:
random shuffle
missing data
narrow dataset cardinality
data science
heart failure
Online Access:https://www.frontiersin.org/articles/10.3389/fcvm.2020.599923/full

Internet

https://www.frontiersin.org/articles/10.3389/fcvm.2020.599923/full

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