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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Detalles Bibliográficos
Autores principales: Fassina, Lorenzo, Faragli, Alessandro, Lo Muzio, Francesco Paolo, Kelle, Sebastian, Campana, Carlo, Pieske, Burkert, Edelmann, Frank, Alogna, Alessio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Cardiovascular Medicine
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7714902/
https://www.ncbi.nlm.nih.gov/pubmed/33330661
http://dx.doi.org/10.3389/fcvm.2020.599923

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7714902/
https://www.ncbi.nlm.nih.gov/pubmed/33330661
http://dx.doi.org/10.3389/fcvm.2020.599923

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