machine learning
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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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Machine Learning: Predicting Early Outcomes of Antidepressants in Children
In this podcast, we are joined by Dr. Paul Croarkin and Dr. Arjun Athreya to discuss their co-authored JCPP paper ‘Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants’.
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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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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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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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