Track topics on Twitter Track topics that are important to you
Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy.
This article was published in the following journal.
Name: JMIR mental health
Individuals with autism spectrum disorder struggle with motor difficulties throughout the life span, and these motor difficulties may affect independent living skills and quality of life. Yet, we kno...
The Centers for Disease Control and Prevention (CDC) coordinates a labor-intensive process to measure the prevalence of autism spectrum disorder (ASD) among children in the United States. Random fores...
To compare the reported accuracy and sensitivity of the various modalities used to diagnose autism spectrum disorders (ASD) in efforts to help focus further biomarker research on the most promising me...
Autism spectrum disorder (ASD) is a heterogeneous disorder. Research has explored potential ASD subgroups with preliminary evidence supporting the existence of behaviorally and genetically distinct su...
The primary aim of this study is to compare the diagnostic accuracy of an iPad application (Play.Care assessment) with the current clinical "gold standard" diagnosis for diagnosis of Autis...
The purpose of this study is to test whether Direct Instruction - Language for Learning (DI-LL) is an effective way to teach language skills to children with autism spectrum disorder (ASD)...
The investigators aim at offering to about 80 patients a diagnostic assessment for Autism Spectrum Disorder (ASD), and at testing the feasibility of this protocol to detect and diagnose AS...
Use of the software could allow learning of reading code in subjects with ASD through the design of serious games for educational and therapeutic purposes. This SEMATIC software could imp...
The overall goal of this research is to use neurophysiological measures to profile strengths and deficits for Attention Deficit Hyperactivity Disorder co-morbidity in Autism Spectrum Disor...
SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data.
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.
A type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when exposed to new data.
The process of gaining approval by a government regulatory agency for DIAGNOSTIC REAGENTS AND TEST KITS. This includes any required preclinical or clinical testing, review, submission, and evaluation of the applications and test results, and post-marketing surveillance.
Adhd Anorexia Depression Dyslexia Mental Health Psychiatry Schizophrenia Stress Mental health, although not being as obvious as physical health, is very important, causing great unhappiness to those affected, causing add...