Psychiatry

M₂DC: A Meta-Learning Framework for Generalizable Diagnostic Classification of Major Depressive Disorder.

Psychiatric diseases are bringing heavy burdens for both individual health and social stability. The accurate and timely diagnosis of the diseases is essential for effective treatment and intervention. Thanks to the rapid development of brain imaging technology and machine learning algorithms, diagnostic classification of psychiatric diseases can be achieved based on brain images. However, due to ...

A machine learning model using clinical notes to estimate PHQ-9 symptom severity scores in depressed patients.

BACKGROUND: Lack of widespread use of the Patient Health Questionnaire 9-item (PHQ-9) in clinical practice inhibits measurement of treatment follow-up for patients with major depressive disorder (MDD). This study developed, validated and applied a machine learning model to estimate PHQ-9 scores for MDD patients using relevant notes from electronic medical records (EMR).

Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network.

Previous deep learning-based brain network research has made significant progress in understanding the pathophysiology of schizophrenia. However, it i...

AFMDD: Analyzing Functional Connectivity Feature of Major Depressive Disorder by Graph Neural Network-Based Model.

The extraction of biomarkers from functional connectivity (FC) in the brain is of great significance for the diagnosis of mental disorders. In recent ...

Conventional and machine learning-based analysis of age, body weight and body height significance in knot position-related thyrohyoid and cervical spine fractures in suicidal hangings.

The thyrohyoid complex and cervical spine fracture distribution patterns may reflect the knot position as the force distribution by the noose to diffe...

Using natural language processing to identify patterns associated with depression, anxiety, and stress symptoms during the COVID-19 pandemic.

BACKGROUND: Combining data-driven natural language processing techniques with traditional methods using predefined word lists may offer greater insigh...

AI-based non-invasive imaging technologies for early autism spectrum disorder diagnosis: A short review and future directions.

Autism Spectrum Disorder (ASD) is a neurological condition, with recent statistics from the CDC indicating a rising prevalence of ASD diagnoses among ...

Optimal selection of diagnostic method for diabetes mellitus using complex bipolar fuzzy dynamic data.

Diabetes mellitus refers to a collection of metabolic disorders that affect the way carbohydrates are processed in the body. It is a prominent worldwi...

Machine Learning Tool for New Selective Serotonin and Serotonin-Norepinephrine Reuptake Inhibitors.

Depression, a serious mood disorder, affects about 5% of the population. Currently, there are two groups of antidepressants that are the first-line tr...

Artificial intelligence conversational agents in mental health: Patients see potential, but prefer humans in the loop.

BACKGROUND: Digital mental health interventions, such as artificial intelligence (AI) conversational agents, hold promise for improving access to care...

Interventions to improve medication adherence in persons with mental disorders.

PURPOSE OF REVIEW: Nonadherence to medication is prevalent in patients with mental illness. Various factors responsible for it. As a result, there are...

Improving explainability of post-separation suicide attempt prediction models for transitioning service members: insights from the Army Study to Assess Risk and Resilience in Servicemembers - Longitudinal Study.

Risk of U.S. Army soldier suicide-related behaviors increases substantially after separation from service. As universal prevention programs have been ...

Estimating the Prevalence of Schizophrenia in the General Population of Japan Using an Artificial Neural Network-Based Schizophrenia Classifier: Web-Based Cross-Sectional Survey.

BACKGROUND: Estimating the prevalence of schizophrenia in the general population remains a challenge worldwide, as well as in Japan. Few studies have ...

An Explainable Artificial Intelligence Text Classifier for Suicidality Prediction in Youth Crisis Text Line Users: Development and Validation Study.

BACKGROUND: Suicide represents a critical public health concern, and machine learning (ML) models offer the potential for identifying at-risk individu...

Exploring the Credibility of Large Language Models for Mental Health Support: Protocol for a Scoping Review.

BACKGROUND: The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GP...

Marigold: a machine learning-based web app for zebrafish pose tracking.

BACKGROUND: High-throughput behavioral analysis is important for drug discovery, toxicological studies, and the modeling of neurological disorders suc...

Machine learning for the diagnosis accuracy of bipolar disorder: a systematic review and meta-analysis.

BACKGROUND: Diagnosing bipolar disorder poses a challenge in clinical practice and demands a substantial time investment. With the growing utilization...

Exploring the application of AI in the education of children with autism: a public health perspective.

INTRODUCTION: Autism Spectrum Disorder (ASD) presents significant challenges in social communication and interaction, critically impacting the lives o...

Mental health phenotypes of well-controlled HIV in Uganda.

INTRODUCTION: The phenotypic expression of mental health (MH) conditions among people with HIV (PWH) in Uganda and worldwide are heterogeneous. Accord...

Preliminary investigation of an artificial intelligence-based cognitive behavioral therapy training tool.

We developed an asynchronous online cognitive behavioral therapy (CBT) training tool that provides artificial intelligence- (AI-) enabled feedback to ...