Psychiatry

Classifying Obsessive-Compulsive Disorder from Resting-State EEG using Convolutional Neural Networks: A Pilot Study

Objective: Identifying obsessive-compulsive disorder (OCD) using brain data remains challenging. Resting-state electroencephalography (EEG) offers an affordable and noninvasive approach, but identifying predictive signals in EEG data has met with little success, even with the application of traditional machine learning methods. We explored whether convolutional neural networks (CNNs) applied to EE...

Development, System Design, Safety, and Performance Metrics of a Conversational Agent for Reducing Depressive and Anxious Symptoms Based on a Large Language Model: The MHAI Study

Conversational agents based on large language models (LLMs) have shown moderate efficacy in reducing depressive and anxiety symptoms. However, most existing evaluations lack methodological transparency, rely on closed-source models, and show limited standardization in performance and safety assessment. We have two study objectives: (1) to develop an LLM-based conversational agent through system de...

New Model, Old Risks? Sociodemographic Bias and Adversarial Hallucinations Vulnerability in GPT-5

Extending our validated benchmarking work, GPT-5 showed no improvement in sociodemographic-linked decision variation compared with GPT-4o and seemed t...

Predictive Modelling of Depression Treatment Response using Individual Symptoms and Latent Factors

Machine learning models have increasingly been used to identify predictors of treatment response in depression, and it is hoped that they may eventual...

Leveraging fMRI non-stationarity for deep learning classifier training and feature detection to improve schizophrenia diagnosis

A neurobiologically-based diagnosis with superior reliability in place of clinical interview-based diagnosis is a primary goal in psychiatry. Dynamic ...

Does later chronotype cause poorer adolescent mental health? An Adolescent Brain Cognitive Development (ABCD) Study

This study investigated whether chronotype (biobehavioral preference for sleep and wake timing) across early adolescence impacts mental health symptom...

Neurocomputational evidence of sustained Self-Other mergence after psychedelics

Mental illness is often characterised by a maladaptive sense of self. The neurobiological basis of Self-Other distinction may provide targets for ther...

Clinical trials in depression: Integrated collection across EU and US registries

Depression affects millions worldwide with both pharmacological and psychological therapies widely applied, both with limited treatment success. Many ...

Privacy-preserving local language models accurately identify the presence and timing of self-harm in electronic mental health records

Self-harm, defined as intentional self-injury or self-poisoning irrespective of motivation, is the strongest risk factor for suicide and an important ...

Speech Acoustic Markers Detect APOE-ε4 Carrier Status in Cognitively Healthy Individuals

APOE-ε4, the strongest genetic risk factor for Alzheimer’s disease (AD), is linked to early motor vulnerability, including subtle speech control chang...

KG2ML: Integrating Knowledge Graphs and Positive Unlabeled Learning for Identifying Disease-Associated Genes

Biomedical knowledge graphs (KGs), such as the Data Distillery Knowledge Graph (DDKG), capture known relationships among entities (e.g., genes, diseas...

Predicting Relapse and Psychosocial Functioning in Major Depressive Disorder: A Machine Learning Approach Using Clinical and Resting-State fMRI Data

Machine learning approaches pave a promising avenue to advance individual predictions about psychiatric illnesses, possibly using biomarkers. Here, we...

Machine learning to phenotype pain and predict response to pain interventions among young adults with irritable bowel syndrome

Irritable bowel syndrome (IBS) is a prevalent disorder whose most debilitating symptom is pain. The complex, multifactorial nature of IBS pain leads t...

Mining medical narratives on geriatric falls to predict post-fall hospitalization via survival models and large language models

Timely admission to the emergency department is a crucial determinant of patient outcomes. Conversely, unnecessary hospital admissions can overburden ...

Evaluating Large Language Models for ADHD Education: A Comparative Study of ChatGPT-5, DeepSeek V3, and Grok 4

Children with attention-deficit/hyperactivity disorder (ADHD) often face barriers to participating in organized sports, particularly when physical edu...

Leveraging Reddit data for Context-enhanced Synthetic Health Data Generation to Identify Low Self Esteem

Low self-esteem (LoST) is a latent yet critical psychosocial risk factor that predisposes individuals to depressive disorders. Although structured too...

EAGLE-AI: A large language model workflow for automated extraction and scoring of literature evidence linking genes to autism spectrum disorder

We previously developed the Evaluation of Autism Gene Link Evidence (EAGLE) manual curation framework and used it to characterise 219 autism-associate...

Autonomous conversational agents for loneliness, social isolation, depression and anxiety in older people without cognitive impairment: Systematic review and meta-analysis

Loneliness is a major psychological challenge in older adulthood, contributing to increased risks of depression, anxiety, and mortality. Conversationa...

Fragile X Syndrome in Brazil: Development and Validation of a Clinical Checklist for Population Screening

Fragile X Syndrome (FXS) is the most common inherited cause of intellectual disability and syndromic autism, but diagnosis remains challenging due to ...

Modelling Approaches for Predicting the Distribution of Skin NTDs: A Systematic Review

Skin neglected tropical diseases (NTDs) such as cutaneous leishmaniasis, lymphatic filariasis, mycetoma, and podoconiosis affect millions in endemic r...