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

Year Number of Results
1961 1
1963 1
1966 1
1973 1
1974 2
1975 1
1976 2
1977 4
1978 1
1979 2
1980 5
1981 1
1982 1
1983 6
1984 9
1985 6
1986 5
1987 8
1988 3
1989 5
1990 10
1991 14
1992 8
1993 8
1994 15
1995 20
1996 10
1997 34
1998 41
1999 41
2000 91
2001 144
2002 160
2003 191
2004 295
2005 347
2006 395
2007 458
2008 482
2009 581
2010 865
2011 1039
2012 1161
2013 1323
2014 1387
2015 1725
2016 1709
2017 1603
2018 1834
2019 1866
2020 1961
2021 2047
2022 2259
2023 1945
2024 881

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24,200 results

Results by year

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Page 1
Data mining in clinical big data: the frequently used databases, steps, and methodological models.
Wu WT, Li YJ, Feng AZ, Li L, Huang T, Xu AD, Lyu J. Wu WT, et al. Mil Med Res. 2021 Aug 11;8(1):44. doi: 10.1186/s40779-021-00338-z. Mil Med Res. 2021. PMID: 34380547 Free PMC article. Review.
Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases. This article introduced the main medical public database and described the steps, tasks, and models of data mining in …
Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public da …
The role of data science in healthcare advancements: applications, benefits, and future prospects.
Subrahmanya SVG, Shetty DK, Patil V, Hameed BMZ, Paul R, Smriti K, Naik N, Somani BK. Subrahmanya SVG, et al. Ir J Med Sci. 2022 Aug;191(4):1473-1483. doi: 10.1007/s11845-021-02730-z. Epub 2021 Aug 16. Ir J Med Sci. 2022. PMID: 34398394 Free PMC article. Review.
Data science is an interdisciplinary field that extracts knowledge and insights from many structural and unstructured data, using scientific methods, data mining techniques, machine-learning algorithms, and big data. ...The process …
Data science is an interdisciplinary field that extracts knowledge and insights from many structural and unstructured data, us …
Clinical data mining.
Zeng QT, Fodeh S. Zeng QT, et al. Comput Biol Med. 2015 Jul;62:293. doi: 10.1016/j.compbiomed.2015.05.014. Comput Biol Med. 2015. PMID: 26067752 No abstract available.
Using data mining techniques to characterize participation in observational studies.
Linden A, Yarnold PR. Linden A, et al. J Eval Clin Pract. 2016 Dec;22(6):835-843. doi: 10.1111/jep.12515. Epub 2016 Jan 25. J Eval Clin Pract. 2016. PMID: 26805004 Free article.
Data mining techniques are gaining in popularity among health researchers for an array of purposes, such as improving diagnostic accuracy, identifying high-risk patients and extracting concepts from unstructured data. ...Moreover, unlike the other mode …
Data mining techniques are gaining in popularity among health researchers for an array of purposes, such as improving d …
Post-acquisition data mining techniques for LC-MS/MS-acquired data in drug metabolite identification.
Dhurjad PS, Marothu VK, Rathod R. Dhurjad PS, et al. Bioanalysis. 2017 Aug;9(16):1265-1278. doi: 10.4155/bio-2017-0046. Epub 2017 Aug 17. Bioanalysis. 2017. PMID: 28816067
Fortunately, advancements in data mining techniques have simplified the process of data interpretation with improved mass accuracy and provide a potentially selective, sensitive, accurate and comprehensive way for metabolite identification. ...We have …
Fortunately, advancements in data mining techniques have simplified the process of data interpretation with impr …
Classification of Skin Disease using Ensemble Data Mining Techniques.
Verma AK, Pal S, Kumar S. Verma AK, et al. Asian Pac J Cancer Prev. 2019 Jun 1;20(6):1887-1894. doi: 10.31557/APJCP.2019.20.6.1887. Asian Pac J Cancer Prev. 2019. PMID: 31244314 Free PMC article.
Methods: In this research paper, we present a new method, which applies five different data mining techniques and then developed an ensemble approach that consists all the five different data mining techniques as a single unit. We …
Methods: In this research paper, we present a new method, which applies five different data mining techniques an …
Deep learning for mining protein data.
Shi Q, Chen W, Huang S, Wang Y, Xue Z. Shi Q, et al. Brief Bioinform. 2021 Jan 18;22(1):194-218. doi: 10.1093/bib/bbz156. Brief Bioinform. 2021. PMID: 31867611 Review.
The recent emergence of deep learning to characterize complex patterns of protein big data reveals its potential to address the classic challenges in the field of protein data mining. ...This review provides comprehensive perspectives on general deep learning …
The recent emergence of deep learning to characterize complex patterns of protein big data reveals its potential to address the class …
Data Processing and Text Mining Technologies on Electronic Medical Records: A Review.
Sun W, Cai Z, Li Y, Liu F, Fang S, Wang G. Sun W, et al. J Healthc Eng. 2018 Apr 8;2018:4302425. doi: 10.1155/2018/4302425. eCollection 2018. J Healthc Eng. 2018. PMID: 29849998 Free PMC article. Review.
However, EMR has the characteristics of diversity, incompleteness, redundancy, and privacy, which make it difficult to carry out data mining and analysis directly. Therefore, it is necessary to preprocess the source data in order to improve data qualit …
However, EMR has the characteristics of diversity, incompleteness, redundancy, and privacy, which make it difficult to carry out data …
Glycoinformatics: data mining-based approaches.
Mamitsuka H. Mamitsuka H. Chimia (Aarau). 2011;65(1-2):10-3. doi: 10.2533/chimia.2011.10. Chimia (Aarau). 2011. PMID: 21469437 Free article. Review.
Due to long-term efforts by experimentalists, the current number of structurally different, determined carbohydrates has exceeded 10,000 or more. As a result data mining-based approaches for glycans (or trees in a computer science sense) have attracted attention and …
Due to long-term efforts by experimentalists, the current number of structurally different, determined carbohydrates has exceeded 10,000 or …
24,200 results
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