Аpplication of Big Data in laboratory medicine. Russian Journal for Personalized Medicine
https://doi.org/10.18705/2782-3806-2023-3-4-77-87
Abstract
The term “big data” (Big Data) refers to data sets, covering the excessive difference in differences between databases in the storage, management and analysis of information. The emergence of big data application algorithms has become the consumption of resources that use resource resources for information processing and computer calculations for the purpose of big data for statistical processing, analysis, forecasting and decision making. In laboratory practice, with a large amount of practical digital information, the use of big data is not currently widespread. The purpose of this work is to conduct a retrospective review of the literature on the use of big data in the field of laboratory medicine in the period 2018–2023. and evaluating the results of practical developments, benefits and achievements associated with big data analytics in the field of laboratory.
About the Authors
M. A. Ovchinnikova
Almazov National Medical Research Centre
Russian Federation
Russian Federation
Ovchinnikova Marina A. - resident of the Department of Laboratory Medicine and Genetics
Akkuratova str., 2, Saint Petersburg, 197341
Yu. I. Zhilenkova
Almazov National Medical Research Centre; North-Western State Medical University named after I. I. Mechnikov
Russian Federation
Russian Federation
Zhilenkova Yuliya I. - PhD., Associate Professor, Department of Laboratory Medicine and Genetics
Saint Petersburg
N. Yu. Chernysh
Almazov National Medical Research Centre; Russian Research Institute of Hematology and Transfusiology of the Federal Medical and
Biological Agency
Russian Federation
Russian Federation
Chernysh Natalia Yu. - PhD., Associate Professor, Department of Laboratory Medicine and Genetics
Saint Petersburg
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Review
For citations:
Ovchinnikova M.A., Zhilenkova Yu.I., Chernysh N.Yu. Аpplication of Big Data in laboratory medicine. Russian Journal for Personalized Medicine. Russian Journal for Personalized Medicine. 2023;3(4):77-87. (In Russ.) https://doi.org/10.18705/2782-3806-2023-3-4-77-87
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