Toward a literature-driven definition of big data in healthcare - Université de Lille Accéder directement au contenu
Article Dans Une Revue BioMed Research International Année : 2015

Toward a literature-driven definition of big data in healthcare

Résumé

OBJECTIVE: The aim of this study was to provide a definition of big data in healthcare. METHODS: A systematic search of PubMed literature published until May 9, 2014, was conducted. We noted the number of statistical individuals (n) and the number of variables (p) for all papers describing a dataset. These papers were classified into fields of study. Characteristics attributed to big data by authors were also considered. Based on this analysis, a definition of big data was proposed. RESULTS: A total of 196 papers were included. Big data can be defined as datasets with Log(n∗p) ≥ 7. Properties of big data are its great variety and high velocity. Big data raises challenges on veracity, on all aspects of the workflow, on extracting meaningful information, and on sharing information. Big data requires new computational methods that optimize data management. Related concepts are data reuse, false knowledge discovery, and privacy issues. CONCLUSIONS: Big data is defined by volume. Big data should not be confused with data reuse: data can be big without being reused for another purpose, for example, in omics. Inversely, data can be reused without being necessarily big, for example, secondary use of Electronic Medical Records (EMR) data.
Fichier principal
Vignette du fichier
639021.pdf (1.37 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte
Loading...

Dates et versions

hal-02527485 , version 1 (01-04-2020)

Identifiants

Citer

Emilie Baro, Samuel Degoul, Regis Beuscart, Emmanuel Chazard. Toward a literature-driven definition of big data in healthcare. BioMed Research International , 2015, ⟨10.1155/2015/639021⟩. ⟨hal-02527485⟩

Collections

UNIV-LILLE
60 Consultations
67 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More