Review
Version 2
Preserved in Portico This version is not peer-reviewed
Big Data in Laboratory Medicine – FAIR Quality for AI?
Version 1
: Received: 24 March 2022 / Approved: 8 April 2022 / Online: 8 April 2022 (10:52:31 CEST)
Version 2 : Received: 13 July 2022 / Approved: 15 July 2022 / Online: 15 July 2022 (05:00:44 CEST)
Version 2 : Received: 13 July 2022 / Approved: 15 July 2022 / Online: 15 July 2022 (05:00:44 CEST)
A peer-reviewed article of this Preprint also exists.
Blatter, T.U.; Witte, H.; Nakas, C.T.; Leichtle, A.B. Big Data in Laboratory Medicine—FAIR Quality for AI? Diagnostics 2022, 12, 1923. Blatter, T.U.; Witte, H.; Nakas, C.T.; Leichtle, A.B. Big Data in Laboratory Medicine—FAIR Quality for AI? Diagnostics 2022, 12, 1923.
Abstract
Laboratory medicine is a digital science. Every large hospital produces a wealth of data each day - from simple numerical results from e.g. sodium measurements to highly complex output of “-omics” analyses, as well as quality control results and meta-data. Processing, connecting, storing, and ordering extensive parts of these individual data requires Big Data techniques. Whereas novel technologies such as artificial intelligence and machine learning have exciting application for the augmentation of laboratory medicine, the Big Data concept remains fundamental for any sophisticated data analysis in large databases. To make laboratory medicine data optimally usable for clinical and research purposes, they need to be FAIR: findable, accessible, interoperable, and reusable. This can be achieved for example by automated recording, connection of devices, efficient ETL (Extract, Transform, Load) processes, careful data governance, and modern data security solutions. Enriched with clinical data, laboratory medicine data allow a gain in pathophysiological insights, can improve patient care, or they can be used to develop reference intervals for diagnostic purposes. Nevertheless, Big Data in laboratory medicine do not come without challenges: The growing number of analyses and data derived from them is a demanding task to be taken care of. Laboratory medicine experts are and will be needed to drive this development, take an active role in the ongoing digitalization, and provide guidance for their clinical colleagues engaging with the laboratory data in research.
Keywords
digitalization; clinical chemistry; artificial intelligence; interoperability; FAIRification
Subject
Medicine and Pharmacology, Pathology and Pathobiology
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Commenter: Tobias Ueli Blatter
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