Psychiatry

Bridging Brain Signals and Self-Reported Symptoms: An AI-Driven, High-Sensitivity Model for Detecting Suicidality in Major Depressive Disorder

Major depressive disorder (MDD) with suicidality represents a significant public health concern, as suicide ranks among the leading causes of death worldwide. While electroencephalography (EEG) has shown promise in depression diagnosis, its utility in identifying suicidal risk remains underexplored. This study aims to develop and validate a Suicidal Risk Index (SR Index) using EEG biomarkers and m...

Integrating Infection Burden and Multimodal Biomarkers for Early Detection of Alzheimers Disease: A Sheaf-ML Framework

Alzheimers disease (AD) remains a major global health challenge, with growing evidence linking chronic infections, immune aging, and neurodegeneration. Grounded in the Antimicrobial Protection Hypothesis, this study introduces a sheaf-theoretic machine learning framework, Sheaf-ML, for integrating multimodal health data and assessing infection-related cognitive risk. Sheaf-ML constructs a unified ...

Information Leakage and Performance Overestimation in EEG-Based Schizophrenia Detection: Evidence from Literature and Empirical Analyses

Detecting schizophrenia (SZ) from electroencephalography (EEG) signals using machine- and deep learning models gained traction lately due to potential...

Shared genetic architecture of brain age gap across 30 cohorts worldwide

Deviations from normative brain ageing trajectories are linked to a wide range of adverse health outcomes. A number of brain age prediction models hav...

STELLA: Safety Testing Engine for Large Language Assistants

Assistants incorporating large language models are increasingly applied in the context of health care, where they represent a promising means of expan...

An informatics approach to profiling patient experiences using electronic health records: constructing and clustering the burden space of individuals under 65 years of age with multiple long-term conditions

Living with multiple long-term conditions (MLTC) profoundly impacts patients’ lives, affecting not only their health but also their financial, emotion...

Multimodal MRI Marker of Cognition Explains the Association Between Cognition and Mental Health in UK Biobank

Cognitive dysfunction often co-occurs with psychopathology. Advances in neuroimaging and machine learning have led to neural indicators that predict i...

Evaluation of large language model chatbot responses to psychotic prompts

The large language model (LLM) chatbot product ChatGPT has accumulated 800 million weekly users since its 2022 launch. In 2025, several media outlets ...

A foundation model for mapping the phenomic and genetic landscape of cerebral small vessel disease biomarkers

Cerebral small vessel disease (CSVD) is a leading cause of age-related cognitive decline and neurological disorders, yet its precise characterization ...

Clustering high-cost patients in England using machine learning: a population-based cohort study

To identify clusters of high-cost patients in England based on diagnoses and sociodemographic characteristics to inform targeted population health man...

Machine learning-optimized perinatal depression screening: Maximum impact, minimal burden

Perinatal depression affects up to 30% of pregnant and postpartum women, which has increased since the COVID-19 pandemic, making rapidly identifying a...

Mapping Neurochemical Signatures onto Brain Structure for Neurotransmitter-Informed Discrimination of Schizophrenia Patients from Healthy Controls

Schizophrenia (SCZ) is associated with widespread gray matter volume (GMV) reductions, yet the underlying mechanisms driving these alterations remain ...

Development and Evaluation of Machine Learning Models to Predict Mechanical Restraint and Related Coercive Measures in Hospital Psychiatry

Use of coercive measures in psychiatric hospitals is clinically and ethically challenging. Aiming to support prevention, we developed and evaluated ma...

Effect and Mechanisms of a Voice-based Coach using AI on Psychological Distress: A Phase 2 Randomized Trial

Artificial Intelligence (AI) voice applications have the potential to address the unmet treatment needs among patients with depression and anxiety, bu...

Development and Validation of Machine Learning-Based Prediction of Depression Progression Using EHR Data: A Multi-Institutional Retrospective Cohort Study

Depression is a leading cause of global disability. Timely identification of patients at risk for clinical worsening remains a major challenge. Electr...

Detecting Mental Disorders in Social Media Using a Transformer-Based Ensemble of Binary Classifiers

This study introduces a novel transformer-based ensemble framework for the multi-label detection of mental health disorders from social media posts. U...

Mapping heterogeneity in the neuroanatomical correlates of depression

Major depressive disorder (MDD) affects millions worldwide, yet its neurobiological underpinnings remain elusive. Neuroimaging studies have yielded in...

Antidepressant Use at the Threshold: using electronic health records to characterise people prescribed antidepressants around the time of dementia diagnosis

Antidepressant use is common in people with dementia. Antidepressants may be started to manage symptoms of dementia, rather than depressive and anxiet...

A Chatbot for the Management of Bipolar Disorder: Using Retrieval-Augmented Generation with an Open-Weight Large Language Model to Answer Clinical Questions Based on the CANMAT and ISBD 2018 Guidelines

Clinical practice guidelines support evidence-based care but are often underused due to complexity, time constraints, and navigation challenges. We in...

TRUSTING: An International Multicenter Observational Study of Speech-Based Relapse Prediction in Psychosis Using Explainable AI

The course of psychotic disorders typically involves relapses. Early warning signs vary between individuals and are difficult to detect in clinical pr...