Psychiatry

Using Machine Learning to Identify Suicide Risk: A Classification Tree Approach to Prospectively Identify Adolescent Suicide Attempters.

This study applies classification tree analysis to prospectively identify suicide attempters among a large adolescent community sample, to demonstrate the strengths and limitations of this approach for risk identification. Data were drawn from the National Longitudinal Study of Adolescent to Adult Health. Youth (n = 4,834, M = 16.15, SD = 1.63, 52.3% female, 63.7% White) completed at-home intervie...

Early Detection of Depression: Social Network Analysis and Random Forest Techniques.

BACKGROUND: Major depressive disorder (MDD) or depression is among the most prevalent psychiatric disorders, affecting more than 300 million people globally. Early detection is critical for rapid intervention, which can potentially reduce the escalation of the disorder.

Using heart rate profiles during sleep as a biomarker of depression.

BACKGROUND: Abnormalities in heart rate during sleep linked to impaired neuro-cardiac modulation may provide new information about physiological sleep...

Prediction models for high risk of suicide in Korean adolescents using machine learning techniques.

OBJECTIVE: Suicide in adolescents is a major problem worldwide and previous history of suicide ideation and attempt represents the strongest predictor...

SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse.

Synapses are fundamental information-processing units of the brain, and synaptic dysregulation is central to many brain disorders ("synaptopathies"). ...

Machine learning identifies unaffected first-degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients.

Schizophrenia (SCZ) patients and their unaffected first-degree relatives (FDRs) share similar functional neuroanatomy. However, it remains largely unk...

Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal.

BACKGROUND: Prediction of therapeutic outcome of repetitive transcranial magnetic stimulation (rTMS) treatment is an important purpose that eliminates...

Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis.

A psychological disorder is a mutilation state of the body that intervenes the imperative functioning of the mind or brain. In the last few years, the...

Deep Sequential Models for Suicidal Ideation From Multiple Source Data.

This paper presents a novel method for predicting suicidal ideation from electronic health records (EHR) and ecological momentary assessment (EMA) dat...

Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.

We address the challenge of detecting the contribution of noncoding mutations to disease with a deep-learning-based framework that predicts the specif...

See your mental state from your walk: Recognizing anxiety and depression through Kinect-recorded gait data.

As the challenge of mental health problems such as anxiety and depression increasing today, more convenient, objective, real-time assessing techniques...

Real-time analysis of the behaviour of groups of mice via a depth-sensing camera and machine learning.

Preclinical studies of psychiatric disorders use animal models to investigate the impact of environmental factors or genetic mutations on complex trai...

Identifying psychosis spectrum disorder from experience sampling data using machine learning approaches.

The ubiquity of smartphones opened up the possibility of widespread use of the Experience Sampling Method (ESM). The method is used to collect longitu...

nCREANN: Nonlinear Causal Relationship Estimation by Artificial Neural Network; Applied for Autism Connectivity Study.

Quantifying causal (effective) interactions between different brain regions are very important in neuroscience research. Many conventional methods est...

Identification and analysis of behavioral phenotypes in autism spectrum disorder via unsupervised machine learning.

BACKGROUND AND OBJECTIVE: Autism spectrum disorder (ASD) is a heterogeneous disorder. Research has explored potential ASD subgroups with preliminary e...

Artificial Intelligence based facial recognition for Mood Charting among men on life style modification and it's correlation with cortisol.

UNLABELLED: Today, clinicians and researchers believe that mood disorders in children and adolescents remain one of the most under diagnosed mental he...

Psychosocial Health Interventions by Social Robots: Systematic Review of Randomized Controlled Trials.

BACKGROUND: Social robots that can communicate and interact with people offer exciting opportunities for improved health care access and outcomes. How...

Your Robot Therapist Will See You Now: Ethical Implications of Embodied Artificial Intelligence in Psychiatry, Psychology, and Psychotherapy.

BACKGROUND: Research in embodied artificial intelligence (AI) has increasing clinical relevance for therapeutic applications in mental health services...

Proactive Suicide Prevention Online (PSPO): Machine Identification and Crisis Management for Chinese Social Media Users With Suicidal Thoughts and Behaviors.

BACKGROUND: Suicide is a great public health challenge. Two hundred million people attempt suicide in China annually. Existing suicide prevention prog...

Detecting depression using a framework combining deep multimodal neural networks with a purpose-built automated evaluation.

Machine learning (ML) has been introduced into the medical field as a means to provide diagnostic tools capable of enhancing accuracy and precision wh...