Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Sep;27(3):307-308.
doi: 10.1111/camh.12546. Epub 2022 Feb 26.

Technology Matters: Machine learning approaches to personalised child and adolescent mental health care

Lewis W Paton  1 , Paul A Tiffin  1   2
Affiliations

Technology Matters: Machine learning approaches to personalised child and adolescent mental health care

Lewis W Paton et al. Child Adolesc Ment Health. 2022 Sep.

Abstract

There has been much interest in the potential for machine learning and artificial intelligence to enhance health care. In this article, we discuss the potential applications of the technology to child and adolescent mental health services (CAMHS). We also outline the four key criteria that are likely to be necessary for automated prediction to be translated into clinical benefit. These relate to the choice of task to be automated, the nature of the available data, the methods applied and the context of the system to be implemented.

Keywords: Machine learning; artificial intelligence; digital health; personalised medicine.

PubMed Disclaimer

Similar articles

See all similar articles

Cited by

References

    1. Abbas, H., Garberson, F., Glover, E., & Wall, D.P. (2018). Machine learning approach for early detection of autism by combining questionnaire and home video screening. Journal of the American Medical Informatics Association, 25, 1000-1007.
    1. Care Quality Commission (2021). The state of health care and adult social care in England 2020/2021. www.cqc.org.uk/sites/default/files/20211021_stateofcare2021_print.pdf
    1. Chekroud, A.M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., … & Iniesta, R. (2021). The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry, 20, 154-170.
    1. Fitzpatrick, K.K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Mental Health, 4, e19.
    1. Tiffin, P.A., & Paton, L.W. (2018). Rise of the machines? Machine learning approaches and mental health: Opportunities and challenges. British Journal of Psychiatry, 213, 509-510.

LinkOut - more resources