Literature DB >> 28918390

A glossary for big data in population and public health: discussion and commentary on terminology and research methods.

Daniel Fuller1, Richard Buote2, Kevin Stanley3.   

Abstract

The volume and velocity of data are growing rapidly and big data analytics are being applied to these data in many fields. Population and public health researchers may be unfamiliar with the terminology and statistical methods used in big data. This creates a barrier to the application of big data analytics. The purpose of this glossary is to define terms used in big data and big data analytics and to contextualise these terms. We define the five Vs of big data and provide definitions and distinctions for data mining, machine learning and deep learning, among other terms. We provide key distinctions between big data and statistical analysis methods applied to big data. We contextualise the glossary by providing examples where big data analysis methods have been applied to population and public health research problems and provide brief guidance on how to learn big data analysis methods. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Entities:  

Keywords:  methodology; public health; research methods

Mesh:

Year:  2017        PMID: 28918390     DOI: 10.1136/jech-2017-209608

Source DB:  PubMed          Journal:  J Epidemiol Community Health        ISSN: 0143-005X            Impact factor:   3.710


  9 in total

1.  Good times bad times: Automated forecasting of seasonal cryptosporidiosis in Ontario using machine learning.

Authors:  Olaf Berke; Lise Trotz-Williams; Simon de Montigny
Journal:  Can Commun Dis Rep       Date:  2020-06-04

Review 2.  Challenges in the development of digital public health interventions and mapped solutions: Findings from a scoping review.

Authors:  Ihoghosa Iyamu; Oralia Gómez-Ramírez; Alice Xt Xu; Hsiu-Ju Chang; Sarah Watt; Geoff Mckee; Mark Gilbert
Journal:  Digit Health       Date:  2022-05-26

3.  Nutrient composition databases in the age of big data: foodDB, a comprehensive, real-time database infrastructure.

Authors:  Richard Andrew Harrington; Vyas Adhikari; Mike Rayner; Peter Scarborough
Journal:  BMJ Open       Date:  2019-06-27       Impact factor: 2.692

4.  A Delphi study to build consensus on the definition and use of big data in obesity research.

Authors:  Christina Vogel; Stephen Zwolinsky; Claire Griffiths; Matthew Hobbs; Emily Henderson; Emma Wilkins
Journal:  Int J Obes (Lond)       Date:  2019-01-17       Impact factor: 5.095

5.  Investigating the transmission risk of infectious disease outbreaks through the Aotearoa Co-incidence Network (ACN): a population-based study.

Authors:  S M Turnbull; M Hobbs; L Gray; E P Harvey; W M L Scarrold; D R J O'Neale
Journal:  Lancet Reg Health West Pac       Date:  2022-01-01

Review 6.  Analytical Challenges and Metrological Approaches to Ensuring Dietary Supplement Quality: International Perspectives.

Authors:  Alessandra Durazzo; Barbara C Sorkin; Massimo Lucarini; Pavel A Gusev; Adam J Kuszak; Cindy Crawford; Courtney Boyd; Patricia A Deuster; Leila G Saldanha; Bill J Gurley; Pamela R Pehrsson; James M Harnly; Aida Turrini; Karen W Andrews; Andrea T Lindsey; Michael Heinrich; Johanna T Dwyer
Journal:  Front Pharmacol       Date:  2022-01-11       Impact factor: 5.810

7.  Why machine learning (ML) has failed physical activity research and how we can improve.

Authors:  Daniel Fuller; Reed Ferber; Kevin Stanley
Journal:  BMJ Open Sport Exerc Med       Date:  2022-03-16

8.  Toward Systems Models for Obesity Prevention: A Big Role for Big Data.

Authors:  Adele R Tufford; Christos Diou; Desiree A Lucassen; Ioannis Ioakimidis; Grace O'Malley; Leonidas Alagialoglou; Evangelia Charmandari; Gerardine Doyle; Konstantinos Filis; Penio Kassari; Tahar Kechadi; Vassilis Kilintzis; Esther Kok; Irini Lekka; Nicos Maglaveras; Ioannis Pagkalos; Vasileios Papapanagiotou; Ioannis Sarafis; Arsalan Shahid; Pieter van 't Veer; Anastasios Delopoulos; Monica Mars
Journal:  Curr Dev Nutr       Date:  2022-07-30

9.  How Do Emotions during Goal Pursuit in Weight Change over Time? Retrospective Computational Text Analysis of Goal Setting and Striving Conversations with a Coach during a Mobile Weight Loss Program.

Authors:  Heather Behr; Annabell Suh Ho; Ellen Siobhan Mitchell; Qiuchen Yang; Laura DeLuca; Andreas Michealides
Journal:  Int J Environ Res Public Health       Date:  2021-06-19       Impact factor: 3.390

  9 in total

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