Psychiatry

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

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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 ...

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 ...

Prediction and prioritization of autism-associated long non-coding RNAs using gene expression and sequence features.

BACKGROUND: Autism spectrum disorders (ASD) refer to a range of neurodevelopmental conditions, which...

Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis.

BACKGROUND: Poorly managed pain can lead to substance use disorders, depression, suicide, worsening ...

The opportunities and challenges of machine learning in the acute care setting for precision prevention of posttraumatic stress sequelae.

Personalized medicine is among the most exciting innovations in recent clinical research, offering t...

Single Volume Image Generator and Deep Learning-Based ASD Classification.

Autism spectrum disorder (ASD) is an intricate neuropsychiatric brain disorder characterized by soci...

Construction of embedded fMRI resting-state functional connectivity networks using manifold learning.

We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional...

Classification of Depression Through Resting-State Electroencephalogram as a Novel Practice in Psychiatry: Review.

BACKGROUND: Machine learning applications in health care have increased considerably in the recent p...

Use of machine learning to classify adult ADHD and other conditions based on the Conners' Adult ADHD Rating Scales.

A reliable diagnosis of adult Attention Deficit/Hyperactivity Disorder (ADHD) is challenging as many...

Deep learning with wearable based heart rate variability for prediction of mental and general health.

The ubiquity and commoditisation of wearable biosensors (fitness bands) has led to a deluge of perso...

Big data in severe mental illness: the role of electronic monitoring tools and metabolomics.

There is an increasing interest in the development of effective early detection and intervention str...

Automated identification of postural control for children with autism spectrum disorder using a machine learning approach.

It is unclear whether postural sway characteristics could be used as diagnostic biomarkers for autis...

Robot-assisted extraperitoneal para-aortic lymphadenectomy (RAePAL) performed with the bipolar cutting method.

OBJECTIVE: In comparison with laparoscopic transperitoneal para-aortic lymphadenectomy, the advantag...

Using Machine Learning to Predict Suicide Attempts in Military Personnel.

Identifying predictors of suicide attempts is critical in intervention and prevention efforts, yet f...

EEG-based deep learning model for the automatic detection of clinical depression.

Clinical depression is a neurological disorder that can be identified by analyzing the Electroenceph...

Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder.

With the advancement of brain imaging techniques and a variety of machine learning methods, signific...

A Randomized Study Using Telepresence Robots for Behavioral Health in Interprofessional Practice and Education.

The events of the coronavirus disease 2019 (COVID-19) pandemic forced the world to adopt telemedici...

Learning Individualized Treatment Rules for Multiple-Domain Latent Outcomes.

For many mental disorders, latent mental status from multiple-domain psychological or clinical sympt...

Resting-state connectome-based support-vector-machine predictive modeling of internet gaming disorder.

Internet gaming disorder (IGD), a worldwide mental health issue, has been widely studied using neuro...

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