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Stable gene selection by self-representation method in fuzzy sample classification.

Medical & biological engineering & computing
In recent years, microarray technology and gene expression profiles have been widely used to detect, predict, or classify the samples of various diseases. The presence of large genes in these profiles and the small number of samples are known challen...

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980-2015.

Scientific reports
Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric histo...

Machine Learning Analysis of Digital Clock Drawing Test Performance for Differential Classification of Mild Cognitive Impairment Subtypes Versus Alzheimer's Disease.

Journal of the International Neuropsychological Society : JINS
OBJECTIVE: To determine how well machine learning algorithms can classify mild cognitive impairment (MCI) subtypes and Alzheimer's disease (AD) using features obtained from the digital Clock Drawing Test (dCDT).

Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation.

Journal of medical Internet research
BACKGROUND: Scalable and accurate health outcome prediction using electronic health record (EHR) data has gained much attention in research recently. Previous machine learning models mostly ignore relations between different types of clinical data (i...

Human vs. machine: the psychological and behavioral consequences of being compared to an outperforming artificial agent.

Psychological research
While artificial agents (AA) such as Artificial Intelligence are being extensively developed, a popular belief that AA will someday surpass human intelligence is growing. The present research examined whether this common belief translates into negati...

Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography.

Journal of the American Heart Association
Background Severe, symptomatic aortic stenosis (AS) is associated with poor prognoses. However, early detection of AS is difficult because of the long asymptomatic period experienced by many patients, during which screening tools are ineffective. The...

Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosis.

BMC neurology
BACKGROUND: Evoked potentials (EPs) are a measure of the conductivity of the central nervous system. They are used to monitor disease progression of multiple sclerosis patients. Previous studies only extracted a few variables from the EPs, which are ...

Brain morphological changes in hypokinetic dysarthria of Parkinson's disease and use of machine learning to predict severity.

CNS neuroscience & therapeutics
BACKGROUND: Up to 90% of patients with Parkinson's disease (PD) eventually develop the speech and voice disorder referred to as hypokinetic dysarthria (HD). However, the brain morphological changes associated with HD have not been investigated. Moreo...

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury.

Journal of clinical epidemiology
OBJECTIVE: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury.

Low-contrast X-ray enhancement using a fuzzy gamma reasoning model.

Medical & biological engineering & computing
X-ray images play an important role in providing physicians with satisfactory information correlated to fractures and diseases; unfortunately, most of these images suffer from low contrast and poor quality. Thus, enhancement of the image will increas...