Psychiatry

Future perspectives of robot psychiatry: can communication robots assist psychiatric evaluation in the COVID-19 pandemic era?

PURPOSE OF REVIEW: Direct face-to-face interview between a psychiatrist and a patient is crucial in psychiatric evaluation, however, such traditional interviews are becoming difficult to conduct because of the infection risk in the COVID-19 era. As telepsychiatry, video interviews using internet are suggested to be useful to evaluate and assist individuals with mental disorders. However, some pati...

Robot-induced hallucinations in Parkinson's disease depend on altered sensorimotor processing in fronto-temporal network.

Hallucinations in Parkinson's disease (PD) are disturbing and frequent non-motor symptoms and constitute a major risk factor for psychosis and dementia. We report a robotics-based approach applying conflicting sensorimotor stimulation, enabling the induction of presence hallucinations (PHs) and the characterization of a subgroup of patients with PD with enhanced sensitivity for conflicting sensori...

Research on the influence of attention and emotion of tea drinkers based on artificial neural network.

Tea can help to regulate the mood of human. Based on the influence of tea on people's mood and attention, this study explored the tea concentration wh...

Identification of Suicide Attempt Risk Factors in a National US Survey Using Machine Learning.

IMPORTANCE: Because more than one-third of people making nonfatal suicide attempts do not receive mental health treatment, it is essential to extend s...

Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk.

BACKGROUND: Using novel data mining methods such as natural language processing (NLP) on electronic health records (EHRs) for screening and detecting ...

Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression.

IMPORTANCE: Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improv...

Machine Learning to Differentiate Risk of Suicide Attempt and Self-harm After General Medical Hospitalization of Women With Mental Illness.

BACKGROUND: Suicide prevention is a public health priority, but risk factors for suicide after medical hospitalization remain understudied. This probl...

AI, Virtual Reality, and Robots Advancing Autism Diagnosis and Therapy.

Autism spectrum disorder (ASD) is a challenge in multiple ways. Just getting diagnosed can take months of visits to doctors and specialists. After the...

Brief Report: Can a Composite Heart Rate Variability Biomarker Shed New Insights About Autism Spectrum Disorder in School-Aged Children?

Several studies show altered heart rate variability (HRV) in autism spectrum disorder (ASD), but findings are neither universal nor specific to ASD. W...

Biologically plausible models of neural dynamics for rapid-acting antidepressant interventions.

Machine learning: A primer for psychotherapy researchers.

[Patient-tailored approach in tertiary care expert centres using individual dynamic network analysis].

BACKGROUND: Patients with mental health disorders often have difficulty perceiving associations between multiple symptoms, such as inter-relations bet...

Artificial intelligence: A rapid case for advancement in the personalization of Gynaecology/Obstetric and Mental Health care.

To evaluate and holistically treat the mental health sequelae and potential psychiatric comorbidities associated with obstetric and gynaecological con...

LEGAL AND PRACTICAL PROBLEMS OF USE OF ARTIFICIAL INTELLIGENCE-BASED ROBOTS IN FORENSIC PSYCHIATRY.

OBJECTIVE: The aim: This research aims to analyze the possibilities of using artificial intelligence-based robotics in forensic psychiatry to assess t...

[Study on classification and identification of depressed patients and healthy people among adolescents based on optimization of brain characteristics of network].

To enhance the accuracy of computer-aided diagnosis of adolescent depression based on electroencephalogram signals, this study collected signals of 32...

Machine Learning Analysis of Blood microRNA Data in Major Depression: A Case-Control Study for Biomarker Discovery.

BACKGROUND: There is a lack of reliable biomarkers for major depressive disorder (MDD) in clinical practice. However, several studies have shown an as...

Detecting and Classifying Self-injurious Behavior in Autism Spectrum Disorder Using Machine Learning Techniques.

Traditional self-injurious behavior (SIB) management can place compliance demands on the caregiver and have low ecological validity and accuracy. To s...

[Effects of acupuncture on Nod-like receptor protein 3 inflammasome signal pathway in the prefrontal cortex of rat with depression].

OBJECTIVE: To observe the influence of acupuncture on the expression of pivotal molecules of Nod-like receptor protein 3(NLRP3) inflammasome signal pa...

A Systematic Evaluation of Interneuron Morphology Representations for Cell Type Discrimination.

Quantitative analysis of neuronal morphologies usually begins with choosing a particular feature representation in order to make individual morphologi...

Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging.

A fundamental problem of supervised learning algorithms for brain imaging applications is that the number of features far exceeds the number of subjec...