Using machine learning to impact on long-term clinical care: principles, challenges, and practicalities
Using machine learning to impact on long-term clinical care: principles, challenges, and practicalities
The rise of machine learning in healthcare has significant implications for paediatrics. Long-term conditions with significant disease heterogeneity comprise large portions of the routine work performed by paediatricians. Improving outcomes through discovery of disease and treatment prediction models, alongside novel subgroup clustering of patients, are some of the areas in which machine learning holds significant promise. While artificial intelligence has percolated into routine use in our day to day lives through advertising algorithms, song or movie selections and sifting of spam emails, the ability of machine learning to utilise highly complex and dimensional data has not yet reached its full potential in healthcare. In this review article, we discuss some of the foundations of machine learning, including some of the basic algorithms. We emphasise the importance of correct utilisation of machine learning, including adequate data preparation and external validation. Using nutrition in preterm infants and paediatric inflammatory bowel disease as examples, we discuss the evidence and potential utility of machine learning in paediatrics. Finally, we review some of the future applications, alongside challenges and ethical considerations related to application of artificial intelligence. Impact: Machine learning is a widely used term; however, understanding of the process and application to healthcare is lacking.This article uses clinical examples to explore complex machine learning terms and algorithms.We discuss limitations and potential future applications within paediatrics and neonatal medicine.
algorithms, artificial intelligence, child, humans, infant, newborn, infant, premature, machine learning, medicine
324-333
Ashton, James J.
03369017-99b5-40ae-9a43-14c98516f37d
Young, Aneurin
457b536d-6015-4855-8e4c-0a665a9a2bb1
Johnson, Mark J.
ce07b5dd-b12b-47df-a5df-cd3b9447c9ed
Beattie, R. Mark
9a66af0b-f81c-485c-b01d-519403f0038a
Ashton, James J.
03369017-99b5-40ae-9a43-14c98516f37d
Young, Aneurin
457b536d-6015-4855-8e4c-0a665a9a2bb1
Johnson, Mark J.
ce07b5dd-b12b-47df-a5df-cd3b9447c9ed
Beattie, R. Mark
9a66af0b-f81c-485c-b01d-519403f0038a
Ashton, James J., Young, Aneurin, Johnson, Mark J. and Beattie, R. Mark
(2022)
Using machine learning to impact on long-term clinical care: principles, challenges, and practicalities.
Pediatric Research, 93, .
(doi:10.1038/s41390-022-02194-6).
Abstract
The rise of machine learning in healthcare has significant implications for paediatrics. Long-term conditions with significant disease heterogeneity comprise large portions of the routine work performed by paediatricians. Improving outcomes through discovery of disease and treatment prediction models, alongside novel subgroup clustering of patients, are some of the areas in which machine learning holds significant promise. While artificial intelligence has percolated into routine use in our day to day lives through advertising algorithms, song or movie selections and sifting of spam emails, the ability of machine learning to utilise highly complex and dimensional data has not yet reached its full potential in healthcare. In this review article, we discuss some of the foundations of machine learning, including some of the basic algorithms. We emphasise the importance of correct utilisation of machine learning, including adequate data preparation and external validation. Using nutrition in preterm infants and paediatric inflammatory bowel disease as examples, we discuss the evidence and potential utility of machine learning in paediatrics. Finally, we review some of the future applications, alongside challenges and ethical considerations related to application of artificial intelligence. Impact: Machine learning is a widely used term; however, understanding of the process and application to healthcare is lacking.This article uses clinical examples to explore complex machine learning terms and algorithms.We discuss limitations and potential future applications within paediatrics and neonatal medicine.
Text
s41390-022-02194-6
- Version of Record
More information
Accepted/In Press date: 22 June 2022
e-pub ahead of print date: 29 July 2022
Additional Information:
Funding Information:
J.J.A. is funded by an NIHR clinical lectureship and by an ESPR post-doctoral research grant. A.Y. and M.J.J. are supported by the National Institute for Health Research through the NIHR Southampton Biomedical Research Centre.
Keywords:
algorithms, artificial intelligence, child, humans, infant, newborn, infant, premature, machine learning, medicine
Identifiers
Local EPrints ID: 477166
URI: http://eprints.soton.ac.uk/id/eprint/477166
ISSN: 0031-3998
PURE UUID: 4dd33dbb-dd60-40b9-a81e-de09df0da382
Catalogue record
Date deposited: 31 May 2023 16:30
Last modified: 18 Mar 2024 04:17
Export record
Altmetrics
Contributors
Author:
Aneurin Young
Author:
Mark J. Johnson
Author:
R. Mark Beattie
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics