From big data to better patient outcomes
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00098892%3A_____%2F23%3A10157460" target="_blank" >RIV/00098892:_____/23:10157460 - isvavai.cz</a>
Alternative codes found
RIV/61989592:15110/23:73616681
Result on the web
<a href="https://www.degruyter.com/document/doi/10.1515/cclm-2022-1096/html" target="_blank" >https://www.degruyter.com/document/doi/10.1515/cclm-2022-1096/html</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1515/cclm-2022-1096" target="_blank" >10.1515/cclm-2022-1096</a>
Alternative languages
Result language
angličtina
Original language name
From big data to better patient outcomes
Original language description
Among medical specialties, laboratory medicine is the largest producer of structured data and must play a crucial role for the efficient and safe implementation of big data and artificial intelligence in healthcare. The area of personalized therapies and precision medicine has now arrived, with huge data sets not only used for experimental and research approaches, but also in the "real world". Analysis of real world data requires development of legal, procedural and technical infrastructure. The integration of all clinical data sets for any given patient is important and necessary in order to develop a patient-centered treatment approach. Data-driven research comes with its own challenges and solutions. The Findability, Accessibility, Interoperability, and Reusability (FAIR) Guiding Principles provide guidelines to make data findable, accessible, interoperable and reusable to the research community. Federated learning, standards and ontologies are useful to improve robustness of artificial intelligence algorithms working on big data and to increase trust in these algorithms. When dealing with big data, the univariate statistical approach changes to multivariate statistical methods significantly shifting the potential of big data. Combining multiple omics gives previously unsuspected information and provides understanding of scientific questions, an approach which is also called the systems biology approach. Big data and artificial intelligence also offer opportunities for laboratories and the In Vitro Diagnostic industry to optimize the productivity of the laboratory, the quality of laboratory results and ultimately patient outcomes, through tools such as predictive maintenance and "moving average" based on the aggregate of patient results.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10406 - Analytical chemistry
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Clinical Chemistry and Laboratory Medicine
ISSN
1434-6621
e-ISSN
1437-4331
Volume of the periodical
61
Issue of the periodical within the volume
4
Country of publishing house
DE - GERMANY
Number of pages
7
Pages from-to
580-586
UT code for WoS article
000901733700001
EID of the result in the Scopus database
2-s2.0-85145208418