Machine learning in mental health: a scoping review of methods and applications

ABR Shatte, DM Hutchinson, SJ Teague - Psychological medicine, 2019 - cambridge.org
BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big
data applications for mental health, highlighting current research and applications in …

[HTML][HTML] Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges

AJ Meehan, SJ Lewis, S Fazel, P Fusar-Poli… - Molecular …, 2022 - nature.com
Recent years have seen the rapid proliferation of clinical prediction models aiming to
support risk stratification and individualized care within psychiatry. Despite growing interest …

[HTML][HTML] Improving mental health services: A 50-year journey from randomized experiments to artificial intelligence and precision mental health

L Bickman - Administration and Policy in Mental Health and Mental …, 2020 - Springer
This conceptual paper describes the current state of mental health services, identifies critical
problems, and suggests how to solve them. I focus on the potential contributions of artificial …

Applications of artificial intelligence− machine learning for detection of stress: a critical overview

AFA Mentis, D Lee, P Roussos - Molecular Psychiatry, 2024 - nature.com
Psychological distress is a major contributor to human physiology and pathophysiology, and
it has been linked to several conditions, such as auto-immune diseases, metabolic …

Multi-omics approaches in psychoneuroimmunology and health research: conceptual considerations and methodological recommendations

S Mengelkoch, SMSF Rose, Z Lautman, JC Alley… - Brain, Behavior, and …, 2023 - Elsevier
The field of psychoneuroimmunology (PNI) has grown substantially in both relevance and
prominence over the past 40 years. Notwithstanding its impressive trajectory, a majority of …

[HTML][HTML] Predicting individual cases of major adolescent psychiatric conditions with artificial intelligence

N de Lacy, MJ Ramshaw, E McCauley, KF Kerr… - Translational …, 2023 - nature.com
Three-quarters of lifetime mental illness occurs by the age of 24, but relatively little is known
about how to robustly identify youth at risk to target intervention efforts known to improve …

The use of machine learning techniques in trauma-related disorders: a systematic review

LF Ramos-Lima, V Waikamp… - Journal of psychiatric …, 2020 - Elsevier
Establishing the diagnosis of trauma-related disorders such as Acute Stress Disorder (ASD)
and Posttraumatic Stress Disorder (PTSD) have always been a challenge in clinical practice …

Impact of gender on child and adolescent PTSD

K Garza, T Jovanovic - Current psychiatry reports, 2017 - Springer
Abstract Purpose of Review This review examines the recent literature on biological factors
that influence sex differences in posttraumatic stress disorder (PTSD) during childhood and …

Development and validation of a model to predict posttraumatic stress disorder and major depression after a motor vehicle collision

HN Ziobrowski, CJ Kennedy, B Ustun, SL House… - JAMA …, 2021 - jamanetwork.com
Importance A substantial proportion of the 40 million people in the US who present to
emergency departments (EDs) each year after traumatic events develop posttraumatic stress …

[HTML][HTML] A machine-based prediction model of ADHD using CPT data

O Slobodin, I Yahav, I Berger - Frontiers in human neuroscience, 2020 - frontiersin.org
Despite the popularity of the continuous performance test (CPT) in the diagnosis of attention-
deficit/hyperactivity disorder (ADHD), its specificity, sensitivity, and ecological validity are still …