Future of machine learning in paediatrics.


Journal

Archives of disease in childhood
ISSN: 1468-2044
Titre abrégé: Arch Dis Child
Pays: England
ID NLM: 0372434

Informations de publication

Date de publication:
03 2022
Historique:
received: 01 02 2021
accepted: 16 07 2021
pubmed: 25 7 2021
medline: 11 3 2022
entrez: 24 7 2021
Statut: ppublish

Résumé

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn without being explicitly programmed, through a combination of statistics and computer science. It encompasses a variety of techniques used to analyse and interpret extremely large amounts of data, which can then be applied to create predictive models. Such applications of this technology are now ubiquitous in our day-to-day lives: predictive text, spam filtering, and recommendation systems in social media, streaming video and e-commerce to name a few examples. It is only more recently that ML has started to be implemented against the vast amount of data generated in healthcare. The emerging role of AI in refining healthcare delivery was recently highlighted in the 'National Health Service Long Term Plan 2019'. In paediatrics, workforce challenges, rising healthcare attendance and increased patient complexity and comorbidity mean that demands on paediatric services are also growing. As healthcare moves into this digital age, this review considers the potential impact ML can have across all aspects of paediatric care from improving workforce efficiency and aiding clinical decision-making to precision medicine and drug development.

Identifiants

pubmed: 34301619
pii: archdischild-2020-321023
doi: 10.1136/archdischild-2020-321023
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

223-228

Subventions

Organisme : Wellcome Trust
ID : 203918/Z/16/Z
Pays : United Kingdom
Organisme : Medical Research Council
ID : MRC/R013942/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/P024297/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/R013926/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/R013942/1
Pays : United Kingdom

Informations de copyright

© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: None declared.

Auteurs

Sarah Ln Clarke (SL)

MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
School of Population Health Sciences, University of Bristol, Bristol, UK.
Department of Paediatric Rheumatology, Bristol Royal Hospital for Children, Bristol, UK.

Kevon Parmesar (K)

School of Population Health Sciences, University of Bristol, Bristol, UK.

Moin A Saleem (MA)

Bristol Renal, University of Bristol, Bristol, UK.
Children's Renal Unit, Bristol Royal Hospital for Children, Bristol, UK.

Athimalaipet V Ramanan (AV)

Department of Paediatric Rheumatology, Bristol Royal Hospital for Children, Bristol, UK a.ramanan@bristol.ac.uk.
School of Translational Health Sciences, University of Bristol, Bristol, UK.

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Classifications MeSH