AI Medical Compendium Topic

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

Attention Deficit Disorder with Hyperactivity

Showing 81 to 90 of 91 articles

Clear Filters

Assessing ADHD symptoms in children and adults: evaluating the role of objective measures.

Behavioral and brain functions : BBF
BACKGROUND: Diagnostic guidelines recommend using a variety of methods to assess and diagnose ADHD. Applying subjective measures always incorporates risks such as informant biases or large differences between ratings obtained from diverse sources. Fu...

Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology.

PloS one
Identifying disease genes from a vast amount of genetic data is one of the most challenging tasks in the post-genomic era. Also, complex diseases present highly heterogeneous genotype, which difficult biological marker identification. Machine learnin...

Machine-based classification of ADHD and nonADHD participants using time/frequency features of event-related neuroelectric activity.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Attention-deficit/hyperactivity disorder (ADHD) is the most frequent diagnosis among children who are referred to psychiatry departments. Although ADHD was discovered at the beginning of the 20th century, its diagnosis is still confronted ...

Neurobiological support to the diagnosis of ADHD in stimulant-naïve adults: pattern recognition analyses of MRI data.

Acta psychiatrica Scandinavica
OBJECTIVE: In adulthood, the diagnosis of attention-deficit/hyperactivity disorder (ADHD) has been subject of recent controversy. We searched for a neuroanatomical signature associated with ADHD spectrum symptoms in adults by applying, for the first ...

Predicting clinical symptoms of attention deficit hyperactivity disorder based on temporal patterns between and within intrinsic connectivity networks.

Neuroscience
Attention deficit hyperactivity disorder (ADHD) is a common brain disorder with high prevalence in school-age children. Previously developed machine learning-based methods have discriminated patients with ADHD from normal controls by providing label ...

Crowdsourced validation of a machine-learning classification system for autism and ADHD.

Translational psychiatry
Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) together affect >10% of the children in the United States, but considerable behavioral overlaps between the two disorders can often complicate differential diagnosis. ...

The World Health Organization Adult Attention-Deficit/Hyperactivity Disorder Self-Report Screening Scale for DSM-5.

JAMA psychiatry
IMPORTANCE: Recognition that adult attention-deficit/hyperactivity disorder (ADHD) is common, seriously impairing, and usually undiagnosed has led to the development of adult ADHD screening scales for use in community, workplace, and primary care set...

Network-based classification of ADHD patients using discriminative subnetwork selection and graph kernel PCA.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND: Attention Deficit Hyperactivity Disorder (ADHD) is one of the most prevalent behavioral disorders in childhood and adolescence. Recently, network-based diagnosis of ADHD has attracted great attentions due to the fact that ADHD disease is ...

Use of machine learning for behavioral distinction of autism and ADHD.

Translational psychiatry
Although autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) continue to rise in prevalence, together affecting >10% of today's pediatric population, the methods of diagnosis remain subjective, cumbersome and time inten...

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

PloS one
The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme lea...