Psychiatry

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. Previous work identified a transform of PANSS using an uncorrelated PANSS score matrix (UPSM) to reduce pseudospecificity among symptom domains during clinical trials of schizophrenia. Here we used UPSM-transformed factor scores to identify 5 distinct...

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 associated with various physical disorders and cognitive dysfunction. Consequently, post-stroke symptoms are complex and difficult to understand. In this study, we aimed to clarify the cross-sectional relationship between mood disorders and motor/cognitiv...

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...

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 are genetically complex and heterogeneous with mo...

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

BACKGROUND: Poorly managed pain can lead to substance use disorders, depression, suicide, worsening health, and increased use of health services. Most...

Predicting suicide attempt or suicide death following a visit to psychiatric specialty care: A machine learning study using Swedish national registry data.

BACKGROUND: Suicide is a major public health concern globally. Accurately predicting suicidal behavior remains challenging. This study aimed to use ma...

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 the opportunity for tailored screening and manageme...

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

Autism spectrum disorder (ASD) is an intricate neuropsychiatric brain disorder characterized by social deficits and repetitive behaviors. Deep learnin...

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

We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired...

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 past, and this review focuses on an important appli...

Artificial intelligence for the measurement of vocal stereotypy.

Both researchers and practitioners often rely on direct observation to measure and monitor behavior. When these behaviors are too complex or numerous ...

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 of the symptoms of ADHD resemble symptoms of othe...

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 personal healthcare data, but with limited analytics ty...

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 autism spectrum disorder (ASD). The purpose of this stu...

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

OBJECTIVE: In comparison with laparoscopic transperitoneal para-aortic lymphadenectomy, the advantages of laparoscopic extraperitoneal para-aortic lym...

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 Electroencephalography (EEG) signals. However, the major drawba...

Using Machine Learning to Predict Suicide Attempts in Military Personnel.

Identifying predictors of suicide attempts is critical in intervention and prevention efforts, yet finding predictors has proven difficult due to the ...

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, significant progress has been made in brain disorder diagn...