dc.contributor.author
Fassina, Lorenzo
dc.contributor.author
Faragli, Alessandro
dc.contributor.author
Muzio, Francesco Paolo Lo
dc.contributor.author
Kelle, Sebastian
dc.contributor.author
Campana, Carlo
dc.contributor.author
Pieske, Burkert
dc.contributor.author
Edelmann, Frank
dc.contributor.author
Alogna, Alessio
dc.date.accessioned
2021-04-09T13:02:23Z
dc.date.available
2021-04-09T13:02:23Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/30289
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-30030
dc.description.abstract
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, increasing the problem of missing data. To overcome the issue of a narrow dataset cardinality (in a clinical dataset, the cardinality is the number of patients in that dataset), population-enhancing algorithms are therefore crucial. The aim of this study was to design a random shuffle method to enhance the cardinality of an HF dataset while it is statistically legitimate, without the need of specific hypotheses and regression models. The cardinality enhancement was validated against an established random repeated-measures method with regard to the correctness in predicting clinical conditions and endpoints. In particular, machine learning and regression models were employed to highlight the benefits of the enhanced datasets. The proposed random shuffle method was able to enhance the HF dataset cardinality (711 patients before dataset preprocessing) circa 10 times and circa 21 times when followed by a random repeated-measures approach. We believe that the random shuffle method could be used in the cardiovascular field and in other data science problems when missing data and the narrow dataset cardinality represent an issue.
en
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
random shuffle
en
dc.subject
missing data
en
dc.subject
narrow dataset cardinality
en
dc.subject
data science
en
dc.subject
heart failure
en
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
dc.title
A Random Shuffle Method to Expand a Narrow Dataset and Overcome the Associated Challenges in a Clinical Study: A Heart Failure Cohort Example
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
599923
dcterms.bibliographicCitation.doi
10.3389/fcvm.2020.599923
dcterms.bibliographicCitation.journaltitle
Frontiers in Cardiovascular Medicine
dcterms.bibliographicCitation.originalpublishername
Frontiers Media SA
dcterms.bibliographicCitation.volume
7
refubium.affiliation
Charité - Universitätsmedizin Berlin
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.bibliographicCitation.pmid
33330661
dcterms.isPartOf.eissn
2297-055X