Psychiatry

Schizophrenia versus Healthy Controls Classification based on fMRI 4D Spatiotemporal Data

A wide array of machine learning approaches have been employed for differentiating patients with mental health disorders from healthy controls using neuroimaging data. However, almost all such methods have been applied on inputs based on connectivity matrices or features derived from the neuroimaging data. Only a few papers recently have considered such classification based on the original voxel-b...

Identifying Psychiatric Manifestations in Outpatients with Depression and Anxiety: A Large Language Model-Based Approach

Accurate psychiatric diagnosis and assessment are crucial for effective treatment. However, while current data-driven approaches emphasize diagnostic outcomes, the process of decoding the underlying symptom expressions in patients’ language and mapping them to well-defined psychiatric terminology has received relatively little attention. This study investigates the potential of Large Language Mode...

The lived experience of social anxiety disorder: A conceptual model based on published literature and social media listening

Social anxiety disorder (SAD) affects up to 1 in 8 individuals over their lifetime and is characterized by an intense fear of social situations where ...

Comparison of Machine Learning Models in Predicting Mental Health Sequelae Following Concussion in Youth

Youth who experience concussions may be at greater risk for subsequent mental health challenges, making early detection crucial for timely interventio...

Prognostic predictions in psychosis: exploring the complementary role of machine learning models

Predicting outcomes in schizophrenia spectrum disorders is challenging due to the variability of individual trajectories. While machine learning (ML) ...

Interrelations Between Dopaminergic-, GABAergic- and Glutamatergic Neurotransmitters in Antipsychotic-Naïve Psychosis Patients and the Association to Initial Treatment Response

Preclinical evidence points to disturbances in neural networks in psychosis involving interrelations between dopaminergic-, GABAergic- and glutamaterg...

Advancing the prediction and understanding of placebo responses in chronic back pain using large language models

Placebo analgesia in chronic pain is a widely studied clinical phenomenon, where expectations about the effectiveness of a treatment can result in sub...

Leveraging artificial Intelligence and online psychotherapy to achieve efficient and coordinated services within a healthcare setting: A quality improvement initiative

This study aimed to implement an artificial intelligence-assisted psychiatric triage program, assessing its impact on efficiency and resource optimiza...

Using Artificial Intelligence to Personalize Caring Contact Messages for Recently Discharged Patients: Protocol for a Mixed-Methods Feasibility Study

Suicide risk is substantially elevated following discharge from a psychiatric hospitalization. Caring Contact (CC) messages are brief messages of hope...

MUTATE: A Human Genetic Atlas of Multi-organ AI Endophenotypes using GWAS Summary Statistics

Artificial intelligence (AI) has been increasingly integrated into imaging genetics to provide intermediate phenotypes (i.e., endophenotypes) that bri...

The Impact of Negative Emotions on Adolescents’ Nonsuicidal Self-Injury Thoughts: An Integrated Application of Machine Learning and Multilevel Logistic Models

Non-Suicidal Self-Injury (NSSI) is a prevalent and complex behavior among adolescents, often linked to negative emotions such as loneliness, anxiety, ...

DUNE: a versatile neuroimaging encoder captures brain complexity across three major diseases: cancer, dementia and schizophrenia

Magnetic resonance images (MRI) of the brain exhibit high dimensionality that pose significant challenges for computational analysis. While models pro...

Life Events Extraction From Healthcare Notes for Veteran Acute Suicide Prediction

Predictive models of suicide risk have focused on predictors extracted from structured data found in electronic health records (EHR), with limited con...

Development and validation of a machine learning model to predict cognitive behavioral therapy outcome in obsessive-compulsive disorder using clinical and neuroimaging data

Cognitive behavioral therapy (CBT) is a first-line treatment for obsessive-compulsive disorder (OCD), but clinical response is difficult to predict. I...

Passive sensing with psyche: utilizing data from wearable technology to predict emotion states

Depression and anxiety are some of the most common mental health disorders in the world contributing to significant morbidity and mortality. Past trea...

Predicting Depression in Canadians with or at Risk of Diabetes: A Cross-Sectional Machine Learning Analysis

Depression often goes unrecognized in individuals at risk or living with diabetes, presenting considerable challenges for primary care clinicians. Alt...

Identifying clusters of people with Multiple Long-Term Conditions using Large Language Models: a population-based study

Identifying clusters of people with similar patterns of Multiple Long-Term Conditions (MLTC) could help healthcare services to tailor management for e...

Computational Mechanisms of Approach-Avoidance Conflict Predictively Differentiate Between Affective and Substance Use Disorders

Psychiatric disorders are highly heterogeneous and often co-morbid, posing specific challenges for effective treatment. Recently, computational modeli...

Enhancing Brain Age Estimation Under Uncertainty: A Spectral-normalized Neural Gaussian Process Approach Utilizing 2.5D Slicing

Brain age gap, the difference between estimated brain age and chronological age via magnetic resonance imaging, has emerged as a pivotal biomarker in ...

Characterizing multivariate regional hubs for schizophrenia classification, sex differences, and brain age estimation using explainable AI

To investigate multivariate regional patterns for schizophrenia (SZ) classification, sex differences, and brain age by utilizing structural MRI, demog...