Psychiatry

Latest AI and machine learning research in psychiatry for healthcare professionals.

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Evaluating and modifying the PHDI for depression prevention: insights from NHANES 2005-2018.

BACKGROUND: Depression is a significant focus in mental health research, emerging as a pressing publ...

Early diagnosis of autism across developmental stages through scalable and interpretable ensemble model.

Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition that challenges early ...

A qualitative analysis of college students' interest in mHealth solutions.

This study explores college students' perceptions of an AI-driven mHealth application designed to pr...

Is This Chatbot Safe and Evidence-Based? A Call for the Critical Evaluation of Generative AI Mental Health Chatbots.

The proliferation of artificial intelligence (AI)-based mental health chatbots, such as those on pla...

ChatGPT-4o vs Psychiatrists in Responding to Common Antidepressant Concerns.

PurposeArtificial intelligence (AI) is increasingly integrated into healthcare, including psychiatri...

TTFNet: Temporal-Frequency Features Fusion Network for Speech based Automatic Depression Recognition and Assessment.

Related studies have revealed that the phonological features of depressed patients are different fro...

Contrastive functional connectivity defines neurophysiology-informed symptom dimensions in major depression.

Major depressive disorder (MDD) is highly heterogeneous, posing challenges for effective treatment d...

Occupational mental health: an investigation of risk indicators using interpretable machine learning techniques.

OBJECTIVE: To apply interpretable machine learning to identify key factors influencing work-related ...

Detecting Tardive Dyskinesia Using Video-Based Artificial Intelligence.

Tardive dyskinesia (TD) is a late-onset adverse effect of dopamine receptor-blocking medications, c...

Predicting rTMS treatment response in depression: use of machine learning models to identify the roles of metabolic and clinical factors.

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depres...

Machine Learning and Artificial Intelligence in Suicide Prevention: A Bibliometric Analysis of Emerging Trends and Implications for Nursing.

Nurses play a crucial role in suicide prevention, yet the integration of artificial intelligence and...

Brain stimulation outcome prediction in Major Depressive Disorder by deep learning models using EEG representations.

Major Depressive Disorder (MDD) is known as a widespread illness and needs a timely treatment. The t...

Machine learning models of depression in middle-aged and older adults with cardiovascular metabolic diseases.

BACKGROUND: The incidence of cardiovascular metabolic diseases (CMD) is increasing, and depression i...

A scientometric analysis of machine learning in schizophrenia neuroimaging: Trends and insights (2012-2024).

Machine learning applications in schizophrenia neuroimaging research have undergone significant evol...

Functional connectome-based predictive modeling of suicidal ideation.

Suicide represents an egregious threat to society despite major advancements in medicine, in part du...

Multiband EEG signatures decoded using machine learning for predicting rTMS treatment response in MDD.

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is a promising treatment for major d...

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