Machine learning
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JCPP Editorial: Volume 64, Issue 09, September 2023
Editorial: “Generative artificial intelligence and the ecology of human development” by Carlo Schuengel and Alastair van Heerden
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Identifying non-adult attention-deficit/hyperactivity disorder individuals using a stacked machine learning algorithm using administrative data population registers in a universal healthcare system
Open Access paper from JCPP Advances – ‘This research project aims to build a Machine Learning algorithm (ML) to predict first-time ADHD diagnosis, given that it is the most frequent mental disorder for the non-adult population.’ David Roche et al.
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Can we diagnose mental disorders in children? A large-scale assessment of machine learning on structural neuroimaging of 6916 children in the adolescent brain cognitive development study
Open Access paper from JCPP Advances – ‘Prediction of mental disorders based on neuroimaging is an emerging area of research with promising first results in adults. However, research on the unique demographic of children is underrepresented and it is doubtful whether findings obtained on adults can be transferred to children’. Richard Gaus (pic), Sebastian Pölsterl et al.
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The importance and challenges of improving early identification of language abilities: a commentary on Gasparini et al. (2023)
Open Access paper from the JCPP – ‘Finding early predictors of later language skills and difficulties is fraught with challenges because of the wide developmental variation in language. Gasparini et al. (Journal of Child Psychology and Psychiatry, 2023) aimed to address this issue by applying machine learning methods to parent reports taken from a large longitudinal database (Early Language in Victoria Study). This commentary highlights the advantages and challenges of identifying early predictors of language in this way, and discusses future directions that can build on this important contribution.’ Nicola Botting (pic) and Helen Spicer-Cain
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JCPP Editorial: Volume 64, Issue 01, January 2023
Editorial: ”Safety in numbers’? Big data discovery strategies in neuro-developmental science – contributions and caveats’ by Edmund J.S. Sonuga-Barke
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Using machine-learning methods to identify early-life predictors of 11-year language outcome
Open Access paper from the JCPP – “This study aims to identify a parsimonious set of preschool indicators that predict language outcomes in late childhood, using data from the population-based Early Language in Victoria Study (n = 839)”. Loretta Gasparini et al.
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A machine learning approach identifies unique predictors of borderline personality disorder
Researchers in the USA have identified critical predictors of borderline personality disorder (BPD) in late adolescence, using a machine learning approach. Joseph Beeney and colleagues harnessed data from a large, prospective, longitudinal dataset of >2,400 girls who were evaluated yearly for various clinical, psychosocial and demographic factors.
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Can population registry data predict which children with ADHD are at risk of later substance use disorders?
The first study to examine the potential of machine learning in early prediction of later substance use disorders (SUDs) in youth with ADHD has been published in the Journal of Child Psychiatry and Psychology.
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In Conversation… Prof Argyris Stringaris
Professor Argyris Stringaris discusses his research and the NIMH (National Institute of Mental Health) with freelance Journalist Jo Carlowe. Includes transcription, and links.
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JCPP Editorial: Volume 60, Issue 12, December 2019
“Are computers going to take over: implications of machine learning and computational psychiatry for trainees and practising clinicians” by Argyris Stringaris
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