Psychiatry

Functional connectivity-based classification of autism and control using SVM-RFECV on rs-fMRI data.

Considering the unsatisfactory classification accuracy of autism due to unsuitable features selected in current studies, a functional connectivity (FC)-based algorithm for classifying autism and control using support vector machine-recursive feature elimination (SVM-RFE) is proposed in this paper. The goal is to find the optimal features based on FC and improve the classification accuracy on a lar...

Characterizing functional regional homogeneity (ReHo) as a B-SNIP psychosis biomarker using traditional and machine learning approaches.

BACKGROUND: Recently, a biologically-driven psychosis classification (B-SNIP Biotypes) was derived using brain-based cognitive and electrophysiological markers. Here, we characterized a local functional-connectivity measure, regional homogeneity (ReHo), as a biomarker across Biotypes and conventional DSM diagnoses.

The risks of risk. Regulating the use of machine learning for psychosis prediction.

Recent advances in Machine Learning (ML) have the potential to revolutionise psychosis prediction and psychiatric assessment. This article has two obj...

Multi-view learning-based data proliferator for boosting classification using highly imbalanced classes.

BACKGROUND: Multi-view data representation learning explores the relationship between the views and provides rich complementary information that can i...

Assisted therapeutic system based on reinforcement learning for children with autism.

Assisted therapy is increasingly used in autism spectrum disorders (ASD) for improving social interaction and communication skills in recent years. A ...

Can Machine Learning help us in dealing with treatment resistant depression? A review.

BACKGROUND: About one third of patients treated with antidepressant do not show sufficient symptoms relief and up to 15% of patients remain symptomati...

Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data.

BACKGROUND: Current fMRI-based classification approaches mostly use functional connectivity or spatial maps as input, instead of exploring the dynamic...

The Future of Digital Psychiatry.

PURPOSE OF REVIEW: Treatments in psychiatry have been rapidly changing over the last century, following the development of psychopharmacology and new ...

Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry.

INTRODUCTION: Machine learning capability holds promise to inform disease models, the discovery and development of novel disease modifying therapeutic...

Using street view data and machine learning to assess how perception of neighborhood safety influences urban residents' mental health.

Previous studies have shown that perceptions of neighborhood safety are associated with various mental health outcomes. However, scant attention has b...

Classification of schizophrenia and normal controls using 3D convolutional neural network and outcome visualization.

BACKGROUND: The recent deep learning-based studies on the classification of schizophrenia (SCZ) using MRI data rely on manual extraction of feature ve...

Validation of oxidative stress assay for schizophrenia.

Accumulating evidence implicates oxidative stress in a range of diseases, yet no objective measurement has emerged that characterizes the global natur...

Computer- and Robot-Assisted Therapies to Aid Social and Intellectual Functioning of Children with Autism Spectrum Disorder.

BACKGROUND AND OBJECTIVES: Children with autism spectrum disorder (ASD) experience challenges with social interactions, a core feature of the disorder...

Detection of major depressive disorder from linear and nonlinear heart rate variability features during mental task protocol.

BACKGROUND: Major depressive disorder (MDD) is one of the leading causes of disability; however, current MDD diagnosis methods lack an objective asses...

Predicting death by suicide using administrative health care system data: Can feedforward neural network models improve upon logistic regression models?

BACKGROUND: Suicide is a leading cause of death worldwide. With the increasing volume of administrative health care data, there is an opportunity to e...

Identification of the gene signature reflecting schizophrenia's etiology by constructing artificial intelligence-based method of enhanced reproducibility.

AIMS: As one of the most fundamental questions in modern science, "what causes schizophrenia (SZ)" remains a profound mystery due to the absence of ob...

A difference degree test for comparing brain networks.

Recently, there has been a proliferation of methods investigating functional connectivity as a biomarker for mental disorders. Typical approaches incl...

The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors.

OBJECTIVE: Suicide is a growing public health concern with a global prevalence of approximately 800,000 deaths per year. The current process of evalua...

Use of Social Robots in Mental Health and Well-Being Research: Systematic Review.

BACKGROUND: Technology-assisted clinical interventions are increasingly common in the health care field, often with the proposed aim to improve access...

Deep learning of spontaneous arousal fluctuations detects early cholinergic defects across neurodevelopmental mouse models and patients.

Neurodevelopmental spectrum disorders like autism (ASD) are diagnosed, on average, beyond age 4 y, after multiple critical periods of brain developmen...