Psychiatry

Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors.

In a machine learning setting, this study aims to compare the prognostic utility of connectomic, bra...

Pediatric Acute-Onset Neuropsychiatric Syndrome: A Data Mining Approach to a Very Specific Constellation of Clinical Variables.

Pediatric acute onset neuropsychiatric syndrome (PANS) is a clinically heterogeneous disorder prese...

Detection of Depression and Scaling of Severity Using Six Channel EEG Data.

Depression is a psychiatric problem which affects the growth of a person, like how a person thinks, ...

Psychosocial profiles and their predictors in epilepsy using patient-reported outcomes and machine learning.

OBJECTIVE: To apply unsupervised machine learning to patient-reported outcomes to identify clusters ...

Applying Machine Learning to Kinematic and Eye Movement Features of a Movement Imitation Task to Predict Autism Diagnosis.

Autism is a developmental condition currently identified by experts using observation, interview, an...

Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection.

To solve major limitations in algorithms for the metabolite-based prediction of psychiatric phenotyp...

The Future is Knocking: How Artificial Intelligence Will Fundamentally Change Psychiatry.

Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder.

Deep neural networks have recently been applied to the study of brain disorders such as autism spect...

Supervised Machine Learning: A Brief Primer.

Machine learning is increasingly used in mental health research and has the potential to advance our...

Adherence and acceptability of a robot-assisted Pivotal Response Treatment protocol for children with autism spectrum disorder.

The aim of this study is to present a robot-assisted therapy protocol for children with ASD based on...

A machine learning approach to risk assessment for alcohol withdrawal syndrome.

At present, risk assessment for alcohol withdrawal syndrome relies on clinical judgment. Our aim was...

Identification of Risk Factors Associated with Obesity and Overweight-A Machine Learning Overview.

Social determining factors such as the adverse influence of globalization, supermarket growth, fast ...

Digital conversations about suicide among teenagers and adults with epilepsy: A big-data, machine learning analysis.

OBJECTIVE: Digital media conversations can provide important insight into the concerns and struggles...

Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning.

This study attempted to multimodally measure mental workload and validate indicators for estimating ...

Knowing me, knowing you: theory of mind in AI.

Artificial intelligence has dramatically changed the world as we know it, but is yet to fully embrac...

Towards a brain-based predictome of mental illness.

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years ...

Factors and predictors of length of stay in offenders diagnosed with schizophrenia - a machine-learning-based approach.

BACKGROUND: Prolonged forensic psychiatric hospitalizations have raised ethical, economic, and clini...

Technological advances for the detection of melanoma: Advances in diagnostic techniques.

Managing the balance between accurately identifying early stage melanomas while avoiding obtaining b...

Machine-learning models for depression and anxiety in individuals with immune-mediated inflammatory disease.

OBJECTIVE: Individuals with immune-mediated inflammatory disease (IMID) have a higher prevalence of ...

Deep learning in mental health outcome research: a scoping review.

Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individu...

Classifying heterogeneous presentations of PTSD via the default mode, central executive, and salience networks with machine learning.

Intrinsic connectivity networks (ICNs), including the default mode network (DMN), the central execut...