Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges
Recent years have seen the rapid proliferation of clinical prediction models aiming to
support risk stratification and individualized care within psychiatry. Despite growing interest …
support risk stratification and individualized care within psychiatry. Despite growing interest …
A review of and roadmap for data science and machine learning for the neuropsychiatric phenotype of autism
P Washington, DP Wall - Annual review of biomedical data …, 2023 - annualreviews.org
Autism spectrum disorder (autism) is a neurodevelopmental delay that affects at least 1 in 44
children. Like many neurological disorder phenotypes, the diagnostic features are …
children. Like many neurological disorder phenotypes, the diagnostic features are …
Machine learning-based behavioral diagnostic tools for depression: advances, challenges, and future directions
The psychiatric diagnostic procedure is currently based on self-reports that are subject to
personal biases. Therefore, the diagnostic process would benefit greatly from data-driven …
personal biases. Therefore, the diagnostic process would benefit greatly from data-driven …
A machine learning approach to the diagnosis of autism spectrum disorder and multi-systemic developmental disorder based on retrospective data and ADOS-2 score
M Briguglio, L Turriziani, A Currò, A Gagliano… - Brain Sciences, 2023 - mdpi.com
Early and accurate diagnosis of autism spectrum disorders (ASD) and tailored therapeutic
interventions can improve prognosis. ADOS-2 is a standardized test for ASD diagnosis …
interventions can improve prognosis. ADOS-2 is a standardized test for ASD diagnosis …
Predicting self-harm within six months after initial presentation to youth mental health services: A machine learning study
Background A priority for health services is to reduce self-harm in young people. Predicting
self-harm is challenging due to their rarity and complexity, however this does not preclude …
self-harm is challenging due to their rarity and complexity, however this does not preclude …
Predicting social anxiety in young adults with machine learning of resting-state brain functional radiomic features
BH Kim, MK Kim, HJ Jo, JJ Kim - Scientific reports, 2022 - nature.com
Social anxiety is a symptom widely prevalent among young adults, and when present in
excess, can lead to maladaptive patterns of social behavior. Recent approaches that …
excess, can lead to maladaptive patterns of social behavior. Recent approaches that …
Predictive modelling of deliberate self-harm and suicide attempts in young people accessing primary care: a machine learning analysis of a longitudinal study
Purpose Machine learning (ML) has shown promise in modelling future self-harm but is yet
to be applied to key questions facing clinical services. In a cohort of young people accessing …
to be applied to key questions facing clinical services. In a cohort of young people accessing …
[HTML][HTML] Recent advances in the understanding and psychological treatment of social anxiety disorder
K Wolitzky-Taylor, R LeBeau - Faculty Reviews, 2023 - ncbi.nlm.nih.gov
Social anxiety disorder (SAD) is characterized by persistent anxiety or avoidance of social
situations because of a fear of negative evaluation. Cognitive behavioral therapy …
situations because of a fear of negative evaluation. Cognitive behavioral therapy …
[HTML][HTML] SADXAI: Predicting social anxiety disorder using multiple interpretable artificial intelligence techniques
K Chadaga, S Prabhu, N Sampathila, R Chadaga… - SLAS technology, 2024 - Elsevier
Social anxiety disorder (SAD), also known as social phobia, is a psychological condition in
which a person has a persistent and overwhelming fear of being negatively judged or …
which a person has a persistent and overwhelming fear of being negatively judged or …
Disorder-specific versus transdiagnostic cognitive mechanisms in anxiety and depression: Machine-learning-based prediction of symptom severity
Introduction Psychiatric evaluation of anxiety and depression is currently based on self-
reported symptoms and their classification into discrete disorders. Yet the substantial …
reported symptoms and their classification into discrete disorders. Yet the substantial …