Psychiatry

Exploration and Strategy Analysis of Mental Health Education for Students in Sports Majors in the Era of Artificial Intelligence.

This study aims to explore new educational strategies suitable for the mental health education of college students. Big data and artificial intelligence (AI) are combined to evaluate the mental health education of college students in sports majors. First, the research status on the mental health education of college students is introduced. The internet of things (IoT) on mental health education, a...

An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data.

Functional magnetic resonance imaging (fMRI) as a promising tool to investigate psychotic disorders can be decomposed into useful imaging features such as time courses (TCs) of independent components (ICs) and functional network connectivity (FNC) calculated by TC cross-correlation. TCs reflect the temporal dynamics of brain activity and the FNC characterizes temporal coherence across intrinsic br...

Sentiment Analysis of Statements on Social Media and Electronic Media Using Machine and Deep Learning Classifiers.

When it comes to our everyday life, emotions have a critical role to play. It goes without saying that it is critical in the context of mobile-compute...

Disruptive innovation in psychiatry.

Disruptive innovation is a cornerstone of various disciplines, particularly in the business world, where paradigm-altering approaches are often lauded...

Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms.

Autism spectrum disorder (ASD) is a type of mental illness that can be detected by using social media data and biomedical images. Autism spectrum diso...

Technology Matters: Machine learning approaches to personalised child and adolescent mental health care.

There has been much interest in the potential for machine learning and artificial intelligence to enhance health care. In this article, we discuss the...

Acceptability and Preliminary Results of Technology-Assisted Balance Training in Parkinson's Disease.

(1) Background: Parkinson's Disease (PD) is one of the most common causes of disability among older individuals. The advanced stages of PD are usually...

Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning.

Autism spectrum disorder (ASD) is the fourth most common neurodevelopmental disorder, with a prevalence of 1 in 160 children. Accurate diagnosis relie...

Expressions of anger during advising on life dilemmas predict suicide risk among college students.

Research has demonstrated a relationship between anger and suicidality, while real-time authentic emotions behind facial expressions could be detected...

Deep Learning-based detection of psychiatric attributes from German mental health records.

BACKGROUND: Health care records provide large amounts of data with real-world and longitudinal aspects, which is advantageous for predictive analyses ...

Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning.

Although emerging evidence has implicated structural/functional abnormalities of patients with Autism Spectrum Disorder(ASD), definitive neuroimaging ...

Mood State Detection in Handwritten Tasks Using PCA-mFCBF and Automated Machine Learning.

In this research, we analyse data obtained from sensors when a user handwrites or draws on a tablet to detect whether the user is in a specific mood s...

Comparing Deep Learning and Conventional Machine Learning Models for Predicting Mental Illness from History of Present Illness Notations.

Mental illness, a serious problem across the globe, requires multi-pronged solutions including effective computational models to predict illness. Ment...

Mental speed is high until age 60 as revealed by analysis of over a million participants.

Response speeds in simple decision-making tasks begin to decline from early and middle adulthood. However, response times are not pure measures of men...

Machine learning classification of schizophrenia patients and healthy controls using diverse neuroanatomical markers and Ensemble methods.

Schizophrenia is a major psychiatric disorder that imposes enormous clinical burden on patients and their caregivers. Determining classification bioma...

Flexible Neural Network Realized by the Probabilistic SiO Memristive Synaptic Array for Energy-Efficient Image Learning.

The human brain's neural networks are sparsely connected via tunable and probabilistic synapses, which may be essential for performing energy-efficien...

Can natural language processing models extract and classify instances of interpersonal violence in mental healthcare electronic records: an applied evaluative study.

OBJECTIVE: This paper evaluates the application of a natural language processing (NLP) model for extracting clinical text referring to interpersonal v...

Deep learning model using retinal vascular images for classifying schizophrenia.

Contemporary psychiatric diagnosis still relies on the subjective symptom report of the patient during a clinical interview by a psychiatrist. Given t...

Medically-oriented design for explainable AI for stress prediction from physiological measurements.

BACKGROUND: In the last decade, a lot of attention has been given to develop artificial intelligence (AI) solutions for mental health using machine le...

Automated detection of clinical depression based on convolution neural network model.

As a common mental disorder, depression is placing an increasing burden on families and society. However, the current methods of depression detection ...