AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Obsessive-Compulsive Disorder

Showing 1 to 10 of 18 articles

Clear Filters

Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder.

Scientific reports
Obsessive and Compulsive Symptoms (OCS) or Obsessive Compulsive Disorder (OCD) in the context of schizophrenia or related disorders are of clinical importance as these are associated with a range of adverse outcomes. Natural Language Processing (NLP)...

Identifying the Symptom Severity in Obsessive-Compulsive Disorder for Classification and Prediction: An Artificial Neural Network Approach.

Behavioural neurology
The present study is aimed at identifying the most prominent determinants of OCD along with their strength to classify the OCD patients from healthy controls. The data for this cross-sectional study were collected from 200 diagnosed OCD patients and ...

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

Journal of child and adolescent psychopharmacology
Pediatric acute onset neuropsychiatric syndrome (PANS) is a clinically heterogeneous disorder presenting with: unusually abrupt onset of obsessive compulsive disorder (OCD) or severe eating restrictions, with at least two concomitant cognitive, beha...

Using supervised machine learning on neuropsychological data to distinguish OCD patients with and without sensory phenomena from healthy controls.

The British journal of clinical psychology
OBJECTIVES: While theoretical models link obsessive-compulsive disorder (OCD) with executive function deficits, empirical findings from the neuropsychological literature remain mixed. These inconsistencies are likely exacerbated by the challenge of h...

Application of Hybrid DeepLearning Architectures for Identification of Individuals with Obsessive Compulsive Disorder Based on EEG Data.

Clinical EEG and neuroscience
Obsessive-compulsive disorder (OCD) is a highly common psychiatric disorder. The symptoms of this condition overlap and co-occur with those of other psychiatric illnesses, making diagnosis difficult. The availability of biomarkers could be useful fo...

White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group.

Molecular psychiatry
White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the gener...

Identifying subgroups of urge suppression in Obsessive-Compulsive Disorder using machine learning.

Journal of psychiatric research
Obsessive-compulsive disorder (OCD) is phenomenologically heterogeneous. While predominant models suggest fear and harm prevention drive compulsions, many patients also experience uncomfortable sensory-based urges ("sensory phenomena") that may be as...

Development of an eye-tracking system based on a deep learning model to assess executive function in patients with mental illnesses.

Scientific reports
Patients with mental illnesses, particularly psychosis and obsessive‒compulsive disorder (OCD), frequently exhibit deficits in executive function and visuospatial memory. Traditional assessments, such as the Rey‒Osterrieth Complex Figure Test (RCFT),...

Prediction of pharmacological response in OCD using machine learning techniques and clinical and neuropsychological variables.

Spanish journal of psychiatry and mental health
INTRODUCTION: Obsessive compulsive disorder is associated with affected executive functioning, including memory, cognitive flexibility, and organizational strategies. As it was reported in previous studies, patients with preserved executive functions...

EEG microstate analysis and machine learning classification in patients with obsessive-compulsive disorder.

Journal of psychiatric research
BACKGROUND: Microstate characterization of electroencephalogram (EEG) is a data-driven approach to explore the functional changes and interrelationships of multiple brain networks on a millisecond scale. This study aimed to explore the pathological c...