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

Journal: Child and adolescent mental health
PMID:

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.

Authors

  • Lewis W Paton
    Department of Health Sciences, University of York, UK. Electronic address: lewis.paton@york.ac.uk.
  • Paul A Tiffin
    Department of Health Sciences, University of York, UK; Hull York Medical School, University of York, UK. Electronic address: paul.tiffin@york.ac.uk.