Psychiatry

Corrigendum to "A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly" [J. Affect. Disord. Volume 369, 15 January 2025, Pages 329-337].

Comprehensive evaluation of pipelines for classification of psychiatric disorders using multi-site resting-state fMRI datasets.

Objective classification biomarkers that are developed using resting-state functional magnetic reson...

Psychiatric Genomics 2025: State of the Art and the Path Forward.

Psychiatric genetics has evolved from candidate-gene studies to whole-genome sequencing efforts. Wit...

The use of artificial intelligence in psychotherapy: development of intelligent therapeutic systems.

BACKGROUND: The increasing demand for psychotherapy and limited access to specialists underscore the...

Social media content and suicidality: Implications for practice.

Artificial intelligence is a useful tool for examining suicidality on social media, where people sha...

Neurofind: using deep learning to make individualised inferences in brain-based disorders.

Within precision psychiatry, there is a growing interest in normative models given their ability to ...

Large language models and psychiatry.

Integrating Generative Artificial Intelligence and Large Language Models (LLMs) such as GPT-4 is tra...

AI-Augmented Psychosocial Interventions: A Bibliometric Review and Implications for Nursing.

PURPOSE: To map out the current artificial intelligence (AI)-informed psychosocial interventions res...

Machine Learning-Aided Intelligent Monitoring of Multivariate miRNA Biomarkers Using Bipolar Self-powered Sensors.

Breast cancer has become the most prevalent form of cancer among women on a global scale. The early ...

The predictive role of sedentary behavior and physical activity on adolescent depressive symptoms: A machine learning approach.

OBJECTIVE: This study aims to investigate the predictive value of sedentary behavior and physical ac...

Finding purpose: Integrated latent profile and machine learning analyses identify purpose in life as an important predictor of high-functioning recovery after alcohol treatment.

BACKGROUND: Recent investigations of recovery from alcohol use disorder (AUD) have distinguished sub...

Predicting social anxiety disorder based on communication logs and social network data from a massively multiplayer online game: Using a graph neural network.

AIM: Social anxiety disorder (SAD) is a mental disorder that requires early detection and treatment....

Interpretable modality-specific and interactive graph convolutional network on brain functional and structural connectomes.

Both brain functional connectivity (FC) and structural connectivity (SC) provide distinct neural mec...

Identifying periphery biomarkers of first-episode drug-naïve patients with schizophrenia using machine-learning-based strategies.

Schizophrenia is a complex mental disorder. Accurate diagnosis and classification of schizophrenia h...

Pharmacogenomics and response to lithium in bipolar disorder.

AIMS: The present review explores the existing evidence on pharmacogenomic tests for prediction of l...

Building Trust with AI: How Essential is Validating AI Models in the Therapeutic Triad of Therapist, Patient, and Artificial Third? Comment on What is the Current and Future Status of Digital Mental Health Interventions?

Since the publication of "What is the Current and Future Status of Digital Mental Health Interventio...

Electroencephalogram (EEG) Based Fuzzy Logic and Spiking Neural Networks (FLSNN) for Advanced Multiple Neurological Disorder Diagnosis.

Neurological disorders are a major global health concern that have a substantial impact on death rat...

Validation of a machine learning model for indirect screening of suicidal ideation in the general population.

Suicide is among the leading causes of death worldwide and a concerning public health problem, accou...

Fast, smart, and adaptive: using machine learning to optimize mental health assessment and monitor change over time.

In mental health, accurate symptom assessment and precise measurement of patient conditions are cruc...

Neurobiologically interpretable causal connectome for predicting young adult depression: A graph neural network study.

BACKGROUND: There is a surprising lack of neuroimaging studies of depression that not only identify ...

Utilizing natural language processing for precision prevention of mental health disorders among youth: A systematic review.

BACKGROUND: The global mental health crisis has created barriers to youth mental healthcare, leaving...