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Assessment of Machine Learning Detection of Environmental Enteropathy and Celiac Disease in Children.

08:00 EDT 5th June 2019 | BioPortfolio

Summary of "Assessment of Machine Learning Detection of Environmental Enteropathy and Celiac Disease in Children."

Duodenal biopsies from children with enteropathies associated with undernutrition, such as environmental enteropathy (EE) and celiac disease (CD), display significant histopathological overlap.

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This article was published in the following journal.

Name: JAMA network open
ISSN: 2574-3805
Pages: e195822

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Medical and Biotech [MESH] Definitions

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.

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.

Medical specialty concerned with environmental factors that may impinge upon human disease, and development of methods for the detection, prevention, and control of environmentally related disease.

A primary peripheral T-cell lymphoma in the gastrointestinal tract, most often in the jejunum, associated with a history of CELIAC DISEASE or other gastrointestinal diseases.

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