Psychiatry

Comedic experience with two robots aided a child with autism spectrum disorder to realize the importance of nonverbal communication.

Giving Voice to Vulnerable Children: Machine Learning Analysis of Speech Detects Anxiety and Depression in Early Childhood.

Childhood anxiety and depression often go undiagnosed. If left untreated these conditions, collectively known as internalizing disorders, are associated with long-term negative outcomes including substance abuse and increased risk for suicide. This paper presents a new approach for identifying young children with internalizing disorders using a 3-min speech task. We show that machine learning anal...

Spectral and Temporal Feature Learning With Two-Stream Neural Networks for Mental Workload Assessment.

People's mental workload profoundly affects their work efficiency and health. Mental workload assessment can be used to effectively avoid serious acci...

Towards interpretable machine learning models for diagnosis aid: A case study on attention deficit/hyperactivity disorder.

Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that has heavy consequences on a child's wellbeing, especially in the...

Using Artificial Intelligence to Identify Factors Associated with Autism Spectrum Disorder in Adolescents with Cerebral Palsy.

Autism spectrum disorder (ASD) is common in adolescents with cerebral palsy (CP) and there is a lack of studies applying artificial intelligence to in...

Detecting Developmental Delay and Autism Through Machine Learning Models Using Home Videos of Bangladeshi Children: Development and Validation Study.

BACKGROUND: Autism spectrum disorder (ASD) is currently diagnosed using qualitative methods that measure between 20-100 behaviors, can span multiple a...

Long-term results of monopolar versus bipolar radiofrequency ablation procedure for atrial fibrillation.

BACKGROUND: In this study, we aimed to evaluate the long-term outcomes of monopolar or bipolar radiofrequency ablation concomitant to mitral valve sur...

Spiking Neural Network Modelling Approach Reveals How Mindfulness Training Rewires the Brain.

There has been substantial interest in Mindfulness Training (MT) to understand how it can benefit healthy individuals as well as people with a broad r...

Selection and Optimization of Temporal Spike Encoding Methods for Spiking Neural Networks.

Spiking neural networks (SNNs) receive trains of spiking events as inputs. In order to design efficient SNN systems, real-valued signals must be optim...

Design feasibility of an automated, machine-learning based feedback system for motivational interviewing.

Direct observation of psychotherapy and providing performance-based feedback is the gold-standard approach for training psychotherapists. At present, ...

Identifying as American Indian/Alaska Native in Urban Areas: Implications for Adolescent Behavioral Health and Well-Being.

American Indian and Alaska Native (AI/AN) youth exhibit multiple health disparities, including high rates of alcohol and other drug (AOD) use, violenc...

Predicting anxiety from wholebrain activity patterns to emotional faces in young adults: a machine learning approach.

BACKGROUND: It is becoming increasingly clear that pathophysiological processes underlying psychiatric disorders categories are heterogeneous on many ...

Analyzing DNA methylation patterns in subjects diagnosed with schizophrenia using machine learning methods.

Schizophrenia is a common mental disorder with high heritability. It is genetically complex and to date more than a hundred risk loci have been identi...

Application of Single-Nucleotide Polymorphisms in the Diagnosis of Autism Spectrum Disorders: A Preliminary Study with Artificial Neural Networks.

Autism spectrum disorder (ASD) includes different neurodevelopmental disorders characterized by deficits in social communication, and restricted, repe...

Use of machine learning in predicting clinical response to transcranial magnetic stimulation in comorbid posttraumatic stress disorder and major depression: A resting state electroencephalography study.

BACKGROUND: Repetitive transcranial magnetic stimulation (TMS) is clinically effective for major depressive disorder (MDD) and investigational for oth...

Relative importance of symptoms, cognition, and other multilevel variables for psychiatric disease classifications by machine learning.

This study used machine-learning algorithms to make unbiased estimates of the relative importance of various multilevel data for classifying cases wit...

Evaluating the evidence for biotypes of depression: Methodological replication and extension of.

BACKGROUND: Psychiatric disorders are highly heterogeneous, defined based on symptoms with little connection to potential underlying biological mechan...

Predicting personalized process-outcome associations in psychotherapy using machine learning approaches-A demonstration.

Personalized treatment methods have shown great promise in efficacy studies across many fields of medicine and mental health. Little is known, howeve...

EEG characteristics of children with attention-deficit/hyperactivity disorder.

The electroencephalogram (EEG) is an informative neuroimaging tool for studying attention-deficit/hyperactivity disorder (ADHD); one main goal is to c...

Classifying major depression patients and healthy controls using EEG, eye tracking and galvanic skin response data.

OBJECTIVE: Major depression disorder (MDD) is one of the most prevalent mental disorders worldwide. Diagnosing depression in the early stage is crucia...