Psychiatry

Using Artificial Intelligence to Predict Change in Depression and Anxiety Symptoms in a Digital Intervention: Evidence from a Transdiagnostic Randomized Controlled Trial.

While digital psychiatric interventions reduce treatment barriers, not all persons benefit from this type of treatment. Research is needed to preemptively identify who is likely to benefit from these digital treatments in order to redirect those people to a higher level of care. The current manuscript used an ensemble of machine learning methods to predict changes in major depressive and generaliz...

Detection of Suicidality Among Opioid Users on Reddit: Machine Learning-Based Approach.

BACKGROUND: In recent years, both suicide and overdose rates have been increasing. Many individuals who struggle with opioid use disorder are prone to suicidal ideation; this may often result in overdose. However, these fatal overdoses are difficult to classify as intentional or unintentional. Intentional overdose is difficult to detect, partially due to the lack of predictors and social stigmas t...

Deep-Learning-Based Detection of Infants with Autism Spectrum Disorder Using Auto-Encoder Feature Representation.

Autism spectrum disorder (ASD) is a developmental disorder with a life-span disability. While diagnostic instruments have been developed and qualified...

Identifying individuals with autism spectrum disorder based on the principal components of whole-brain phase synchrony.

Autism spectrum disorder (ASD) is a brain disorder that develops during an early stage of childhood. Previous neuroimaging-based diagnostic models for...

Digital Gaming Interventions in Psychiatry: Evidence, Applications and Challenges.

Human evolution has regularly intersected with technology. Digitalization of various services has brought a paradigm shift in consumerism. Treading th...

Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites.

Structural brain alterations have been repeatedly reported in schizophrenia; however, the pathophysiology of its alterations remains unclear. Multivar...

Moving targets in drug discovery.

Drug Discovery is a lengthy and costly process and has faced a period of declining productivity within the last two decades resulting in increasing im...

On the use of AI for Generation of Functional Music to Improve Mental Health.

Increasingly music has been shown to have both physical and mental health benefits including improvements in cardiovascular health, a link to reductio...

Deep learning for the prediction of treatment response in depression.

BACKGROUND: Mood disorders are characterized by heterogeneity in severity, symptoms and treatment response. The possibility of selecting the correct t...

Deep-Asymmetry: Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression.

To have an objective depression diagnosis, numerous studies based on machine learning and deep learning using electroencephalogram (EEG) have been con...

Robot dramas may improve joint attention of Chinese-speaking low-functioning children with autism: stepped wedge trials.

INTRODUCTION: Children with autism spectrum disorder (ASD), especially those with low cognitive functioning, have deficits in joint attention. Previou...

Comparing machine and deep learning-based algorithms for prediction of clinical improvement in psychosis with functional magnetic resonance imaging.

Previous work using logistic regression suggests that cognitive control-related frontoparietal activation in early psychosis can predict symptomatic i...

Deep learning based classification of facial dermatological disorders.

Common properties of dermatological diseases are mostly lesions with abnormal pattern and skin color (usually redness). Therefore, dermatology is one ...

Integration of a Social Robot in a Pedagogical and Logopedic Intervention with Children: A Case Study.

The effectiveness of social robots such as NAO in pedagogical therapies presents a challenge. There is abundant literature focused on therapies using ...

Effect of Social Anxiety on the Adoption of Robotic Training Partner.

With the development of artificial intelligence technologies, robotic training partner is becoming a reality, which is a substitute for human training...

Characterization of specific and distinct patient types in clinical trials of acute schizophrenia using an uncorrelated PANSS score matrix transform (UPSM).

Understanding the specificity of symptom change in schizophrenia can facilitate the evaluation antipsychotic efficacy for different symptom domains. P...

Relationships between motor and cognitive functions and subsequent post-stroke mood disorders revealed by machine learning analysis.

Mood disorders (e.g. depression, apathy, and anxiety) are often observed in stroke patients, exhibiting a negative impact on functional recovery assoc...

Interpretation of Depression Detection Models via Feature Selection Methods.

Given the prevalence of depression worldwide and its major impact on society, several studies employed artificial intelligence modelling to automatica...

Assessing the predictive ability of the Suicide Crisis Inventory for near-term suicidal behavior using machine learning approaches.

OBJECTIVE: This study explores the prediction of near-term suicidal behavior using machine learning (ML) analyses of the Suicide Crisis Inventory (SCI...

Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review.

According to the World Health Organization (WHO) report in 2016, around 800,000 of individuals have committed suicide. Moreover, suicide is the second...