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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
1957 1
1962 1
1964 4
1965 1
1966 2
1967 1
1968 2
1969 2
1970 2
1972 2
1973 1
1974 2
1975 3
1976 3
1977 1
1978 2
1979 2
1980 4
1981 1
1982 4
1983 3
1984 3
1985 5
1986 6
1987 3
1988 7
1989 8
1990 7
1991 12
1992 15
1993 22
1994 24
1995 32
1996 30
1997 34
1998 31
1999 35
2000 58
2001 82
2002 98
2003 124
2004 188
2005 249
2006 326
2007 414
2008 509
2009 605
2010 736
2011 1182
2012 1523
2013 1953
2014 2440
2015 3321
2016 3949
2017 5301
2018 8352
2019 12694
2020 18260
2021 25696
2022 31480
2023 33922
2024 43944
2025 29542
2026 2

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204,828 results

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Page 1
Introduction to Machine Learning, Neural Networks, and Deep Learning.
Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Choi RY, et al. Transl Vis Sci Technol. 2020 Feb 27;9(2):14. doi: 10.1167/tvst.9.2.14. Transl Vis Sci Technol. 2020. PMID: 32704420 Free PMC article.
PURPOSE: To present an overview of current machine learning methods and their use in medical research, focusing on select machine learning techniques, best practices, and deep learning. METHODS: A systematic literature search in PubMed was perfo …
PURPOSE: To present an overview of current machine learning methods and their use in medical research, focusing on select m …
A guide to machine learning for biologists.
Greener JG, Kandathil SM, Moffat L, Jones DT. Greener JG, et al. Nat Rev Mol Cell Biol. 2022 Jan;23(1):40-55. doi: 10.1038/s41580-021-00407-0. Epub 2021 Sep 13. Nat Rev Mol Cell Biol. 2022. PMID: 34518686 Review.
All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance seem bewildering. ...We describe how different techniques may be suited to specific types of biological data, and also discuss some best practi …
All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance see …
Machine Learning in Medicine.
Deo RC. Deo RC. Circulation. 2015 Nov 17;132(20):1920-30. doi: 10.1161/CIRCULATIONAHA.115.001593. Circulation. 2015. PMID: 26572668 Free PMC article. Review.
The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical da …
The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from …
eDoctor: machine learning and the future of medicine.
Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H. Handelman GS, et al. J Intern Med. 2018 Dec;284(6):603-619. doi: 10.1111/joim.12822. Epub 2018 Sep 3. J Intern Med. 2018. PMID: 30102808 Free article. Review.
Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. ...
Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statist …
Supervised Machine Learning: A Brief Primer.
Jiang T, Gradus JL, Rosellini AJ. Jiang T, et al. Behav Ther. 2020 Sep;51(5):675-687. doi: 10.1016/j.beth.2020.05.002. Epub 2020 May 16. Behav Ther. 2020. PMID: 32800297 Free PMC article. Review.
Machine learning offers new tools to overcome challenges for which traditional statistical methods are not well-suited. This paper provides an overview of machine learning with a specific focus on supervised learning (i.e., methods that are desi …
Machine learning offers new tools to overcome challenges for which traditional statistical methods are not well-suited. This p …
Machine learning model for predicting malaria using clinical information.
Lee YW, Choi JW, Shin EH. Lee YW, et al. Comput Biol Med. 2021 Feb;129:104151. doi: 10.1016/j.compbiomed.2020.104151. Epub 2020 Nov 28. Comput Biol Med. 2021. PMID: 33290932
Various studies have aimed at developing machine learning models to diagnose malaria using blood smear images; however, this approach has many limitations. ...CONCLUSIONS: The results demonstrated that machine learning techniques can be successfully ap …
Various studies have aimed at developing machine learning models to diagnose malaria using blood smear images; however, this a …
Machine learning for cardiology.
Arfat Y, Mittone G, Esposito R, Cantalupo B, DE Ferrari GM, Aldinucci M. Arfat Y, et al. Minerva Cardiol Angiol. 2022 Feb;70(1):75-91. doi: 10.23736/S2724-5683.21.05709-4. Epub 2021 Aug 2. Minerva Cardiol Angiol. 2022. PMID: 34338485 Free article. Review.
We specifically focus on the principal Machine learning based risk scores used in cardiovascular research. After introducing them and summarizing their assumptions and biases, we discuss their merits and shortcomings. We report on how frequently they are adopted in …
We specifically focus on the principal Machine learning based risk scores used in cardiovascular research. After introducing t …
Machine learning for clinical decision support in infectious diseases: a narrative review of current applications.
Peiffer-Smadja N, Rawson TM, Ahmad R, Buchard A, Georgiou P, Lescure FX, Birgand G, Holmes AH. Peiffer-Smadja N, et al. Clin Microbiol Infect. 2020 May;26(5):584-595. doi: 10.1016/j.cmi.2019.09.009. Epub 2019 Sep 17. Clin Microbiol Infect. 2020. PMID: 31539636 Free article. Review.
BACKGROUND: Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID). ...
BACKGROUND: Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literatu …
Review of machine learning and deep learning models for toxicity prediction.
Guo W, Liu J, Dong F, Song M, Li Z, Khan MKH, Patterson TA, Hong H. Guo W, et al. Exp Biol Med (Maywood). 2023 Nov;248(21):1952-1973. doi: 10.1177/15353702231209421. Epub 2023 Dec 6. Exp Biol Med (Maywood). 2023. PMID: 38057999 Free PMC article. Review.
These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using vario …
These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of mac …
Machine Learning Principles for Radiology Investigators.
Borstelmann SM. Borstelmann SM. Acad Radiol. 2020 Jan;27(1):13-25. doi: 10.1016/j.acra.2019.07.030. Acad Radiol. 2020. PMID: 31818379 Review.
Artificial intelligence and deep learning are areas of high interest for radiology investigators at present. However, the field of machine learning encompasses multiple statistics-based techniques useful for investigators, which may be complementary to deep …
Artificial intelligence and deep learning are areas of high interest for radiology investigators at present. However, the field of …
204,828 results
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