AI Medical Compendium Journal:
Journal of child psychology and psychiatry, and allied disciplines

Showing 1 to 10 of 10 articles

Predicting the trajectory of non-suicidal self-injury among adolescents.

Journal of child psychology and psychiatry, and allied disciplines
BACKGROUND: Non-suicidal self-injury (NSSI) is common among adolescents receiving inpatient psychiatric treatment and the months post-discharge is a high-risk period for self-injurious behavior. Thus, identifying predictors that shape the course of p...

Large-scale proteomics in the first trimester of pregnancy predict psychopathology and temperament in preschool children: an exploratory study.

Journal of child psychology and psychiatry, and allied disciplines
BACKGROUND: Understanding the prenatal origins of children's psychopathology is a fundamental goal in developmental and clinical science. Recent research suggests that inflammation during pregnancy can trigger a cascade of fetal programming changes t...

Commentary to "Translational machine learning for child and adolescent psychiatry".

Journal of child psychology and psychiatry, and allied disciplines
In this commentary on 'Translational Machine Learning for Child and Adolescent Psychiatry,' by Dwyer and Koutsouleris, we summarize some of the main points made by the authors, which highlight the importance of emerging applications of machine learni...

Annual Research Review: Translational machine learning for child and adolescent psychiatry.

Journal of child psychology and psychiatry, and allied disciplines
Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the li...

Machine-Learning prediction of comorbid substance use disorders in ADHD youth using Swedish registry data.

Journal of child psychology and psychiatry, and allied disciplines
BACKGROUND: Children with attention-deficit/hyperactivity disorder (ADHD) have a high risk for substance use disorders (SUDs). Early identification of at-risk youth would help allocate scarce resources for prevention programs.

Annual Research Review: Developmental computational psychiatry.

Journal of child psychology and psychiatry, and allied disciplines
Most psychiatric disorders emerge during childhood and adolescence. This is also a period that coincides with the brain undergoing substantial growth and reorganisation. However, it remains unclear how a heightened vulnerability to psychiatric disord...

Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning.

Journal of child psychology and psychiatry, and allied disciplines
BACKGROUND: Adolescents have high rates of nonfatal suicide attempts, but clinically practical risk prediction remains a challenge. Screening can be time consuming to implement at scale, if it is done at all. Computational algorithms may predict suic...

Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion.

Journal of child psychology and psychiatry, and allied disciplines
BACKGROUND: Machine learning (ML) provides novel opportunities for human behavior research and clinical translation, yet its application can have noted pitfalls (Bone et al., 2015). In this work, we fastidiously utilize ML to derive autism spectrum d...

Editorial: Generative artificial intelligence and the ecology of human development.

Journal of child psychology and psychiatry, and allied disciplines
Commercial applications of artificial intelligence (AI) in the form of Large Language Models (LLMs) and Generative AI have taken centre stage in the media sphere, business, public policy, and education. The ramifications for the field of child psycho...

Editorial: Are computers going to take over: implications of machine learning and computational psychiatry for trainees and practising clinicians.

Journal of child psychology and psychiatry, and allied disciplines
Are computers going to take over? In some ways they already have, and they are bound to take over more. As clinicians and researchers, what do we need to do in order to handle, trust, and evaluate intelligent machines?