 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 |
|---|
| 2015 | 4 | 5 | 9 | | 2016 | 0 | 4 | 4 | | 2017 | 9 | 10 | 19 | | 2018 | 10 | 8 | 18 | | 2019 | 18 | 11 | 29 | | 2020 | 19 | 12 | 31 | | 2021 | 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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Campbell TW, Wilson MP, Roder H, MaWhinney S, Georgantas RW, Maguire LK, Roder J, Erlandson KM. Predicting prognosis in COVID-19 patients using machine learning and readily available clinical data. Int J Med Inform. 2021 11; 155:104594.
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D'Alessandro A, Thomas T, Akpan IJ, Reisz JA, Cendali FI, Gamboni F, Nemkov T, Thangaraju K, Katneni U, Tanaka K, Kahn S, Wei AZ, Valk JE, Hudson KE, Roh D, Moriconi C, Zimring JC, Hod EA, Spitalnik SL, Buehler PW, Francis RO. Biological and Clinical Factors Contributing to the Metabolic Heterogeneity of Hospitalized Patients with and without COVID-19. Cells. 2021 09 02; 10(9).
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Porras AR, Bramble MS, Mosema Be Amoti K, Spencer D, Dakande C, Manya H, Vashist N, Likuba E, Ebwel JM, Musasa C, Malherbe H, Mohammed B, Tor-Diez C, Ngoyi DM, Katumbay DT, Linguraru MG, Vilain E. Facial analysis technology for the detection of Down syndrome in the Democratic Republic of the Congo. Eur J Med Genet. 2021 Sep; 64(9):104267.
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Sills MR, Ozkaynak M, Jang H. Predicting hospitalization of pediatric asthma patients in emergency departments using machine learning. Int J Med Inform. 2021 07; 151:104468.
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Li C, Patil V, Rasmussen KM, Yong C, Chien HC, Morreall D, Humpherys J, Sauer BC, Burningham Z, Halwani AS. Predicting Survival in Veterans with Follicular Lymphoma Using Structured Electronic Health Record Information and Machine Learning. Int J Environ Res Public Health. 2021 03 07; 18(5).
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Witeof AE, Daniels AL, Rea LT, Movafaghi S, Kurtz K, Davis M, Eveland RW, Calderon CP, Randolph TW. Machine Learning and Accelerated Stress Approaches to Differentiate Potential Causes of Aggregation in Polyclonal Antibody Formulations During Shipping. J Pharm Sci. 2021 07; 110(7):2743-2752.
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Stevens LM, Linstead E, Hall JL, Kao DP. Association Between Coffee Intake and Incident Heart Failure Risk: A Machine Learning Analysis of the FHS, the ARIC Study, and the CHS. Circ Heart Fail. 2021 02; 14(2):e006799.
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Nemkov T, Stefanoni D, Bordbar A, Issaian A, Palsson BO, Dumont LJ, Hay A, Song A, Xia Y, Redzic JS, Eisenmesser EZ, Zimring JC, Kleinman S, Hansen KC, Busch MP, D'Alessandro A. Blood donor exposome and impact of common drugs on red blood cell metabolism. JCI Insight. 2021 02 08; 6(3).
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Rosen T, Bao Y, Zhang Y, Clark S, Wen K, Elman A, Jeng P, Bloemen E, Lindberg D, Krugman R, Campbell J, Bachman R, Fulmer T, Pillemer K, Lachs M. Identifying patterns of health care utilisation among physical elder abuse victims using Medicare data and legally adjudicated cases: protocol for case-control study using data linkage and machine learning. BMJ Open. 2021 02 05; 11(2):e044768.
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Hale AT, Riva-Cambrin J, Wellons JC, Jackson EM, Kestle JRW, Naftel RP, Hankinson TC, Shannon CN. Machine learning predicts risk of cerebrospinal fluid shunt failure in children: a study from the hydrocephalus clinical research network. Childs Nerv Syst. 2021 05; 37(5):1485-1494.
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