From big data to better patient outcomes.

Autor:	Hulsen T; Department of Hospital Services & Informatics, Philips Research, Eindhoven, The Netherlands., Friedecký D; Department of Clinical Biochemistry, Laboratory for Inherited Metabolic Disorders, University Hospital Olomouc and Faculty of Medicine and Dentistry, Palacký University in Olomouc, Olomouc, Czech Republic., Renz H; Institute of Laboratory Medicine, member of the German Center for Lung Research (DZL), and the Universities of Giessen and Marburg Lung Center (UGMLC), Philipps University Marburg, Marburg, Germany.; Department of Clinical Immunology and Allergy, Laboratory of Immunopathology, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia., Melis E; Ortho Clinical Diagnostics, Zaventem, Belgium., Vermeersch P; Clinical Department of Laboratory Medicine, University Hospitals Leuven, Leuven, Belgium.; Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.; European Federation of Clinical Chemistry and Laboratory Medicine (EFLM), Milan, Italy., Fernandez-Calle P; European Federation of Clinical Chemistry and Laboratory Medicine (EFLM), Milan, Italy.; Department of Laboratory Medicine, Hospital Universitario La Paz, Madrid, Spain.
Jazyk:	angličtina
Zdroj:	Clinical chemistry and laboratory medicine [Clin Chem Lab Med] 2022 Dec 22; Vol. 61 (4), pp. 580-586. Date of Electronic Publication: 2022 Dec 22 (Print Publication: 2023).
DOI:	10.1515/cclm-2022-1096
Abstrakt:	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. (© 2022 Walter de Gruyter GmbH, Berlin/Boston.)
Databáze:	MEDLINE
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