 Machine Learning
"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 type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when exposed to new data.
| Descriptor ID |
D000069550
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| MeSH Number(s) |
G17.035.250.500 L01.224.050.375.530
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| Concept/Terms |
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Below are MeSH descriptors whose meaning is more general than "Machine Learning".
Below are MeSH descriptors whose meaning is more specific than "Machine Learning".
This graph shows the total number of publications written about "Machine Learning" by people in this website by year, and whether "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 |
|---|
| 2014 | 1 | 1 | 2 | | 2015 | 5 | 5 | 10 | | 2016 | 2 | 4 | 6 | | 2017 | 5 | 6 | 11 | | 2018 | 9 | 5 | 14 | | 2019 | 16 | 9 | 25 | | 2020 | 20 | 12 | 32 | | 2021 | 11 | 23 | 34 | | 2022 | 10 | 25 | 35 | | 2023 | 1 | 10 | 11 |
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Below are the most recent publications written about "Machine Learning" by people in Profiles.
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Rita L, Neumann NR, Laponogov I, Gonzalez G, Veselkov D, Pratico D, Aalizadeh R, Thomaidis NS, Thompson DC, Vasiliou V, Veselkov K. Alzheimer's disease: using gene/protein network machine learning for molecule discovery in olive oil. Hum Genomics. 2023 Jul 07; 17(1):57.
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Nguyen B, Torres A, Espinola CW, Sim W, Kenny D, Campbell DM, Lou W, Kapralos B, Beavers L, Peter E, Dubrowski A, Krishnan S, Bhat V. Development of a data-driven digital phenotype profile of distress experience of healthcare workers during COVID-19 pandemic. Comput Methods Programs Biomed. 2023 Oct; 240:107645.
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Yang E, Li MD, Raghavan S, Deng F, Lang M, Succi MD, Huang AJ, Kalpathy-Cramer J. Transformer versus traditional natural language processing: how much data is enough for automated radiology report classification? Br J Radiol. 2023 Sep; 96(1149):20220769.
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Wilde BC, Bragg JG, Cornwell W. Analyzing trait-climate relationships within and among taxa using machine learning and herbarium specimens. Am J Bot. 2023 05; 110(5):e16167.
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Ma?ka M, Ulman V, Delgado-Rodriguez P, G?mez-de-Mariscal E, Necasov? T, Guerrero Pe?a FA, Ren TI, Meyerowitz EM, Scherr T, L?ffler K, Mikut R, Guo T, Wang Y, Allebach JP, Bao R, Al-Shakarji NM, Rahmon G, Toubal IE, Palaniappan K, Lux F, Matula P, Sugawara K, Magnusson KEG, Aho L, Cohen AR, Arbelle A, Ben-Haim T, Raviv TR, Isensee F, J?ger PF, Maier-Hein KH, Zhu Y, Ederra C, Urbiola A, Meijering E, Cunha A, Mu?oz-Barrutia A, Kozubek M, Ortiz-de-Sol?rzano C. The Cell Tracking Challenge: 10?years of objective benchmarking. Nat Methods. 2023 07; 20(7):1010-1020.
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Schold JD. The promise and reality of machine-learning models in kidney transplantation. Kidney Int. 2023 05; 103(5):835-836.
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Callahan TJ, Stefanksi AL, Ostendorf DM, Wyrwa JM, Davies SJD, Hripcsak G, Hunter LE, Kahn MG. Characterizing Patient Representations for Computational Phenotyping. AMIA Annu Symp Proc. 2022; 2022:319-328.
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Mehrpour O, Saeedi F, Vohra V, Abdollahi J, Shirazi FM, Goss F. The role of decision tree and machine learning models for outcome prediction of bupropion exposure: A nationwide analysis of more than 14?000 patients in the United States. Basic Clin Pharmacol Toxicol. 2023 Jul; 133(1):98-110.
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Mehrpour O, Saeedi F, Nakhaee S, Tavakkoli Khomeini F, Hadianfar A, Amirabadizadeh A, Hoyte C. Comparison of decision tree with common machine learning models for prediction of biguanide and sulfonylurea poisoning in the United States: an analysis of the National Poison Data System. BMC Med Inform Decis Mak. 2023 04 06; 23(1):60.
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Gupta S, Kumar S, Chang K, Lu C, Singh P, Kalpathy-Cramer J. Collaborative Privacy-preserving Approaches for Distributed Deep Learning Using Multi-Institutional Data. Radiographics. 2023 04; 43(4):e220107.
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