 Supervised Machine Learning
"Supervised Machine Learning" is a descriptor in the National Library of Medicine's controlled vocabulary thesaurus,
MeSH (Medical Subject Headings). Descriptors are arranged in a hierarchical structure,
which enables searching at various levels of specificity.
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.
| Descriptor ID |
D000069553
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| MeSH Number(s) |
G17.035.250.500.500 L01.224.050.375.530.500
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| Concept/Terms |
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Below are MeSH descriptors whose meaning is more general than "Supervised Machine Learning".
Below are MeSH descriptors whose meaning is more specific than "Supervised Machine Learning".
This graph shows the total number of publications written about "Supervised Machine Learning" by people in this website by year, and whether "Supervised Machine Learning" was a major or minor topic of these publications.
To see the data from this visualization as text, click here.
| Year | Major Topic | Minor Topic | Total |
|---|
| 2016 | 1 | 0 | 1 | | 2019 | 1 | 0 | 1 | | 2020 | 0 | 1 | 1 | | 2021 | 0 | 2 | 2 | | 2023 | 1 | 0 | 1 |
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Below are the most recent publications written about "Supervised Machine Learning" by people in Profiles.
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Heneghan JA, Walker SB, Fawcett A, Bennett TD, Dziorny AC, Sanchez-Pinto LN, Farris RWD, Winter MC, Badke C, Martin B, Brown SR, McCrory MC, Ness-Cochinwala M, Rogerson C, Baloglu O, Harwayne-Gidansky I, Hudkins MR, Kamaleswaran R, Gangadharan S, Tripathi S, Mendonca EA, Markovitz BP, Mayampurath A, Spaeder MC. The Pediatric Data Science and Analytics Subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators Network: Use of Supervised Machine Learning Applications in Pediatric Critical Care Medicine Research. Pediatr Crit Care Med. 2024 Apr 01; 25(4):364-374.
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Xing F, Cornish TC, Bennett TD, Ghosh D. Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images. IEEE Trans Med Imaging. 2021 10; 40(10):2880-2896.
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Appiah SA, Foxx CL, Langgartner D, Palmer A, Zambrano CA, Braum?ller S, Schaefer EJ, Wachter U, Elam BL, Radermacher P, Stamper CE, Heinze JD, Salazar SN, Luthens AK, Arnold AL, Reber SO, Huber-Lang M, Lowry CA, Halbgebauer R. Evaluation of the gut microbiome in association with biological signatures of inflammation in murine polytrauma and shock. Sci Rep. 2021 03 23; 11(1):6665.
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Way GP, Zietz M, Rubinetti V, Himmelstein DS, Greene CS. Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations. Genome Biol. 2020 05 11; 21(1):109.
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To D, Sharma B, Karnik N, Joyce C, Dligach D, Afshar M. Validation of an alcohol misuse classifier in hospitalized patients. Alcohol. 2020 05; 84:49-55.
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Xing F, Cornish TC, Bennett T, Ghosh D, Yang L. Pixel-to-Pixel Learning With Weak Supervision for Single-Stage Nucleus Recognition in Ki67 Images. IEEE Trans Biomed Eng. 2019 11; 66(11):3088-3097.
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Afshar M, Joyce C, Oakey A, Formanek P, Yang P, Churpek MM, Cooper RS, Zelisko S, Price R, Dligach D. A Computable Phenotype for Acute Respiratory Distress Syndrome Using Natural Language Processing and Machine Learning. AMIA Annu Symp Proc. 2018; 2018:157-165.
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Beaulieu-Jones BK, Greene CS. Semi-supervised learning of the electronic health record for phenotype stratification. J Biomed Inform. 2016 12; 64:168-178.
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Dligach D, Miller T, Savova GK. Semi-supervised Learning for Phenotyping Tasks. AMIA Annu Symp Proc. 2015; 2015:502-11.
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