Psychiatry

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

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Comparisons of deep learning and machine learning while using text mining methods to identify suicide attempts of patients with mood disorders.

BACKGROUND: Suicide attempt is one of the most severe consequences for patients with mood disorders....

Research on Mental Health Monitoring Scheme of Migrant Children Based on Convolutional Neural Network Based on Deep Learning.

In recent years, with the acceleration of urbanization and the implementation of compulsory educatio...

Predicting social anxiety in young adults with machine learning of resting-state brain functional radiomic features.

Social anxiety is a symptom widely prevalent among young adults, and when present in excess, can lea...

Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders.

There is growing evidence for the role of DNA methylation (DNAm) quantitative trait loci (mQTLs) in ...

Classification model with weighted regularization to improve the reproducibility of neuroimaging signature selection.

Machine learning (ML) has been extensively applied in brain imaging studies to aid the diagnosis of ...

An End-to-End Human Abnormal Behavior Recognition Framework for Crowds With Mentally Disordered Individuals.

Abnormal or violent behavior by people with mental disorders is common. When individuals with mental...

Uncovering Brain Differences in Preschoolers and Young Adolescents with Autism Spectrum Disorder Using Deep Learning.

Identifying brain abnormalities in autism spectrum disorder (ASD) is critical for early diagnosis an...

Extending Robot Therapy for Children with Autism Using Mobile and Web Application.

Robot treatments for children with autism have proven to be successful and effective. However, the r...

A Case Study of Multiple Maintenance Efficacy in Gynaecological Surgery Assessed by Deep Learning.

Deep learning is a new learning concept and a highly effective way of learning, which is still being...

Explaining Aha! moments in artificial agents through IKE-XAI: Implicit Knowledge Extraction for eXplainable AI.

During the learning process, a child develops a mental representation of the task he or she is learn...

Value-free random exploration is linked to impulsivity.

Deciding whether to forgo a good choice in favour of exploring a potentially more rewarding alternat...

AsthmaKGxE: An asthma-environment interaction knowledge graph leveraging public databases and scientific literature.

MOTIVATION: Asthma is a complex heterogeneous disease resulting from intricate interactions between ...

Controlled Growth of Wafer-Scale Transition Metal Dichalcogenides with a Vertical Composition Gradient for Artificial Synapses with High Linearity.

Artificial synapses are promising for dealing with large amounts of data computing. Great progress h...

Sweat Proteomics in Cystic Fibrosis: Discovering Companion Biomarkers for Precision Medicine and Therapeutic Development.

In clinical routine, the diagnosis of cystic fibrosis (CF) is still challenging regardless of intern...

Language-agnostic deep learning framework for automatic monitoring of population-level mental health from social networks.

In many countries, mental health issues are among the most serious public health concerns. National ...

Sex differences in rTMS treatment response: A deep learning-based EEG investigation.

INTRODUCTION: The present study aimed to investigate sex differences in response to repetitive trans...

Aberrated Multidimensional EEG Characteristics in Patients with Generalized Anxiety Disorder: A Machine-Learning Based Analysis Framework.

Although increasing evidences support the notion that psychiatric disorders are associated with abno...

Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers.

Diagnosis of most neuropsychiatric disorders relies on subjective measures, which makes the reliabil...

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