AIMC Topic: Case-Control Studies

Clear Filters Showing 721 to 730 of 971 articles

Pronation and supination analysis based on biomechanical signals from Parkinson's disease patients.

Artificial intelligence in medicine
In this work, a fuzzy inference model to evaluate hands pronation/supination exercises during the MDS-UPDRS motor examination is proposed to analyze different extracted features from the bio-mechanical signals acquired from patients with Parkinson's ...

Vitamin D status in children with headache: A case-control study.

Clinical nutrition ESPEN
BACKGROUND: Vitamin D is a fat soluble vitamin with hormonal properties, plays crucial functions in bone and mineral metabolism and has important regulatory functions in brain development, cell differentiation and apoptosis. Some studies have shown a...

Support vector machine-based classification of neuroimages in Alzheimer's disease: direct comparison of FDG-PET, rCBF-SPECT and MRI data acquired from the same individuals.

Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999)
OBJECTIVE: To conduct the first support vector machine (SVM)-based study comparing the diagnostic accuracy of T1-weighted magnetic resonance imaging (T1-MRI), F-fluorodeoxyglucose-positron emission tomography (FDG-PET) and regional cerebral blood flo...

Serum levels of chemical elements in esophageal squamous cell carcinoma in Anyang, China: a case-control study based on machine learning methods.

BMJ open
OBJECTIVES: Esophageal squamous cell carcinoma (ESCC) is the predominant form of esophageal carcinoma with extremely aggressive nature and low survival rate. The risk factors for ESCC in the high-incidence areas of China remain unclear. We used machi...

Correcting Classifiers for Sample Selection Bias in Two-Phase Case-Control Studies.

Computational and mathematical methods in medicine
Epidemiological studies often utilize stratified data in which rare outcomes or exposures are artificially enriched. This design can increase precision in association tests but distorts predictions when applying classifiers on nonstratified data. Sev...

A hierarchical classifier based on human blood plasma fluorescence for non-invasive colorectal cancer screening.

Artificial intelligence in medicine
Colorectal cancer (CRC) a leading cause of death by cancer, and screening programs for its early identification are at the heart of the increasing survival rates. To motivate population participation, non-invasive, accurate, scalable and cost-effecti...

Identification of autism spectrum disorder using deep learning and the ABIDE dataset.

NeuroImage. Clinical
The goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the patients brain activation patterns. We investigated ASD patients brain imagi...

Detection of lung cancer in exhaled breath with an electronic nose using support vector machine analysis.

Journal of breath research
Lung cancer is one of the most common malignancies and has a low 5-year survival rate. There are no cheap, simple and widely available screening methods for the early diagnostics of lung cancer. The aim of this study was to determine whether analysis...

Applying machine learning to identify autistic adults using imitation: An exploratory study.

PloS one
Autism spectrum condition (ASC) is primarily diagnosed by behavioural symptoms including social, sensory and motor aspects. Although stereotyped, repetitive motor movements are considered during diagnosis, quantitative measures that identify kinemati...

Measuring Functional Arm Movement after Stroke Using a Single Wrist-Worn Sensor and Machine Learning.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
BACKGROUND AND PURPOSE: Trials of restorative therapies after stroke and clinical rehabilitation require relevant and objective efficacy end points; real-world upper extremity (UE) functional use is an attractive candidate. We present a novel, inexpe...