Psychiatry

Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy.

The study objective was to apply machine learning methodologies to identify predictors of remission in a longitudinal sample of 296 adults with a primary diagnosis of obsessive compulsive disorder (OCD). Random Forests is an ensemble machine learning algorithm that has been successfully applied to large-scale data analysis across vast biomedical disciplines, though rarely in psychiatric research o...

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and...

PsyGeNET: a knowledge platform on psychiatric disorders and their genes.

UNLABELLED: PsyGeNET (Psychiatric disorders and Genes association NETwork) is a knowledge platform for the exploratory analysis of psychiatric disease...

Predicting Methylphenidate Response in ADHD Using Machine Learning Approaches.

BACKGROUND: There are no objective, biological markers that can robustly predict methylphenidate response in attention deficit hyperactivity disorder....

Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM.

Resting state functional Magnetic Resonance Imaging (rs-fMRI) is increasingly used for the identification of image biomarkers of brain diseases or psy...

Negative Affect Instability among Individuals with Comorbid Borderline Personality Disorder and Posttraumatic Stress Disorder.

Ecological momentary assessment (EMA; Stone & Shiffman, 1994) was utilized to examine affective instability (AI) in the daily lives of outpatients wit...

A Survey of Expectations About the Role of Robots in Robot-Assisted Therapy for Children with ASD: Ethical Acceptability, Trust, Sociability, Appearance, and Attachment.

The use of robots in therapy for children with autism spectrum disorder (ASD) raises issues concerning the ethical and social acceptability of this te...

Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism.

Despite consensus on the neurological nature of autism spectrum disorders (ASD), brain biomarkers remain unknown and diagnosis continues to be based o...

Screening Internet forum participants for depression symptoms by assembling and enhancing multiple NLP methods.

Depression is a disease that can dramatically lower quality of life. Symptoms of depression can range from temporary sadness to suicide. Embarrassment...

Predicting long-term outcome of Internet-delivered cognitive behavior therapy for social anxiety disorder using fMRI and support vector machine learning.

Cognitive behavior therapy (CBT) is an effective treatment for social anxiety disorder (SAD), but many patients do not respond sufficiently and a subs...

Bridging a translational gap: using machine learning to improve the prediction of PTSD.

BACKGROUND: Predicting Posttraumatic Stress Disorder (PTSD) is a pre-requisite for targeted prevention. Current research has identified group-level ri...

Abstract computation in schizophrenia detection through artificial neural network based systems.

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that i...

A Mobile Health Application to Predict Postpartum Depression Based on Machine Learning.

BACKGROUND: Postpartum depression (PPD) is a disorder that often goes undiagnosed. The development of a screening program requires considerable and ca...

Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning.

Although the prevalence of autism spectrum disorder (ASD) has risen sharply in the last few years reaching 1 in 68, the average age of diagnosis in th...

Comparison of machine classification algorithms for fibromyalgia: neuroimages versus self-report.

UNLABELLED: Recent studies have posited that machine learning (ML) techniques accurately classify individuals with and without pain solely based on ne...

Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction.

OBJECTIVE: Currently, depression diagnosis relies primarily on behavioral symptoms and signs, and treatment is guided by trial and error instead of ev...

Considering the effects of gender in child-robot interaction studies: comment on Srinivasan, et Al. (2013).

Using a pretest-posttest design, Srinivasan, et al. (2013 ) found that a period of interaction between children and an Isobot humanoid robot improved ...

Using trend templates in a neonatal seizure algorithm improves detection of short seizures in a foetal ovine model.

Seizures below one minute in duration are difficult to assess correctly using seizure detection algorithms. We aimed to improve neonatal detection alg...

Spontaneous motion on two-dimensional continuous attractors.

Attractor models are simplified models used to describe the dynamics of firing rate profiles of a pool of neurons. The firing rate profile, or the neu...

Discrimination of schizophrenia auditory hallucinators by machine learning of resting-state functional MRI.

Auditory hallucinations (AH) are a symptom that is most often associated with schizophrenia, but patients with other neuropsychiatric conditions, and ...