Machine learning
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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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Machine learning improves ADI-R efficiency
Early interventions in autism spectrum disorder (ASD) are essential to improve communication and behavioural skills in affected children. Now, researchers have used machine learning to derive new instrument algorithms that may help practitioners screen for autism more efficiently and effectively.
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Machine learning approach predicts suicide risk
A study has evaluated the performance of machine learning on routinely collected electronic health records, as a possible approach to accurately screen and detect adolescents at risk of making suicide attempts.
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Self-Harm & Suicide Issue – Foreword from the Editor
As a clinician, it certainly does feel that more and more young people are being referred, following self harm or with suicidal ideas, to the CAMHS service I work in. This nationwide increase in numbers is acknowledged in recent government reports, which are summarised in this edition.
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