AI Medical Compendium Topic:
Supervised Machine Learning

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Non-invasive diagnosis of non-alcoholic steatohepatitis and fibrosis with the use of omics and supervised learning: A proof of concept study.

Metabolism: clinical and experimental
BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) affects 25-30% of the general population and is characterized by the presence of non-alcoholic fatty liver (NAFL) that can progress to non-alcoholic steatohepatitis (NASH), liver fibrosis and cirr...

Comparing lesion segmentation methods in multiple sclerosis: Input from one manually delineated subject is sufficient for accurate lesion segmentation.

NeuroImage. Clinical
PURPOSE: Accurate lesion segmentation is important for measurements of lesion load and atrophy in subjects with multiple sclerosis (MS). International MS lesion challenges show a preference of convolutional neural networks (CNN) strategies, such as n...

Predicting individual decision-making responses based on single-trial EEG.

NeuroImage
Decision-making plays an essential role in the interpersonal interactions and cognitive processing of individuals. There has been increasing interest in being able to predict an individual's decision-making response (i.e., acceptance or rejection). W...

Single-slice Alzheimer's disease classification and disease regional analysis with Supervised Switching Autoencoders.

Computers in biology and medicine
BACKGROUND: Alzheimer's disease (AD) is a difficult to diagnose pathology of the brain that progressively impairs cognitive functions. Computer-assisted diagnosis of AD based on image analysis is an emerging tool to support AD diagnosis. In this arti...

Same-day antimicrobial susceptibility test using acoustic-enhanced flow cytometry visualized with supervised machine learning.

Journal of medical microbiology
Antimicrobial susceptibility is slow to determine, taking several days to fully impact treatment. This proof-of-concept study assessed the feasibility of using machine-learning techniques for analysis of data produced by the flow cytometer-assisted ...

Recommendations and future directions for supervised machine learning in psychiatry.

Translational psychiatry
Machine learning methods hold promise for personalized care in psychiatry, demonstrating the potential to tailor treatment decisions and stratify patients into clinically meaningful taxonomies. Subsequently, publication counts applying machine learni...

A semi-supervised machine learning framework for microRNA classification.

Human genomics
BACKGROUND: MicroRNAs (miRNAs) are a family of short, non-coding RNAs that have been linked to critical cellular activities, most notably regulation of gene expression. The identification of miRNA is a cross-disciplinary approach that requires both c...

Multi-label zero-shot human action recognition via joint latent ranking embedding.

Neural networks : the official journal of the International Neural Network Society
Human action recognition is one of the most challenging tasks in computer vision. Most of the existing works in human action recognition are limited to single-label classification. A real-world video stream, however, often contains multiple human act...

Unsupervised and Supervised Learning over theEnergy Landscape for Protein Decoy Selection.

Biomolecules
The energy landscape that organizes microstates of a molecular system and governs theunderlying molecular dynamics exposes the relationship between molecular form/structure, changesto form, and biological activity or function in the cell. However, se...