Psychiatry

Social media content and suicidality: Implications for practice.

Artificial intelligence is a useful tool for examining suicidality on social media, where people share their thoughts. However, existing research has primarily focused on text analysis to predict risk in single posts and raised privacy concerns. This study aimed to use text, images, and user features on Instagram to predict the risk of each user in Hawaii with user permission. One hundred forty-tw...

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 parse heterogeneity. While they are intuitive and informative, the technical expertise and resources required to develop normative models may not be accessible to most researchers. Here we present Neurofind, a new freely available tool that bridges this gap by wrapping sound and previously tested me...

Large language models and psychiatry.

Integrating Generative Artificial Intelligence and Large Language Models (LLMs) such as GPT-4 is transforming clinical medicine and cognitive psycholo...

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

PURPOSE: To map out the current artificial intelligence (AI)-informed psychosocial interventions research landscape, with a focus on main themes, tren...

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 and timely diagnosis of breast cancer is of the ut...

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 activity in adolescent depressive symptoms.

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 subgroups of high and low functioning recovery in dat...

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. However, some individuals with SAD avoid face-to-...

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 mechanisms for cognition and neurological disease. In...

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 has always been a major challenge in clinic due to ...

Pharmacogenomics and response to lithium in bipolar disorder.

AIMS: The present review explores the existing evidence on pharmacogenomic tests for prediction of lithium response in the treatment of bipolar disord...

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 Interventions?" the exponential growth and widespread adoptio...

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 rates and quality of life. accurately identifying a n...

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, accounting for over 700,000 registered deaths worldwide...

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 crucial for clinical decision-making and effective tre...

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 the whole brain causal connectivity features but a...

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 many disorders unaddressed. Precision prevention,...

Development and validation of short-term, medium-term, and long-term suicide attempt prediction models based on a prospective cohort in Korea.

BACKGROUND: This study aimed to develop and validate prediction models for short-(3 months), medium-(1 year), and long-term suicide attempts among hig...

Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia.

DNA methylation (DNAm) is a key epigenetic mark with essential roles in gene regulation, mammalian development, and human diseases. Single-cell techno...

Multi-feature fusion method combining brain functional connectivity and graph theory for schizophrenia classification and neuroimaging markers screening.

BACKGROUND: The abnormalities in brain functional connectivity (FC) and graph topology (GT) in patients with schizophrenia (SZ) are unclear. Researche...