Applied biostatistics in laboratory medicine
Author:
Affiliation:
1. Trillium GmbH Medizinischer Fachverlag , Grafrath , Germany
2. German Heart Center of the Technical University Munich , Munich , Germany
3. Helmholtz Center HZI , Braunschweig , Germany
Publisher
Walter de Gruyter GmbH
Subject
Biochemistry (medical),Clinical Biochemistry,Discrete Mathematics and Combinatorics
Link
https://www.degruyter.com/document/doi/10.1515/labmed-2023-0060/pdf
Reference10 articles.
1. Haeckel, R. Indirect approaches to estimate reference intervals. J Lab Med 2021;45:31–3. https://doi.org/10.1515/labmed-2021-0003.
2. Hoffmann, G, Klawonn, F, Lichtinghagen, R, Orth, M. The zlog value as a basis for the standardization of laboratory results. J Lab Med 2017;41:20170135. https://doi.org/10.1515/labmed-2017-0135.
3. Gruson, D. Big Data, artificial intelligence and laboratory medicine: time for integration. Adv Lab Med 2021;2:1–3. https://doi.org/10.1515/almed-2021-0003.
4. Cadamuro, J, Cabitza, F, Debeljak, Z, De Bruyne, S, Fans, G, Perez, SM, et al.. Potentials and pitfalls of ChatGPT and natural-language artificial intelligence models for the understanding of laboratory medicine test results. Clin Chem Lab Med 2023;61:1158–66. https://doi.org/10.1515/cclm-2023-0355.
5. Adler, J, Lenski, M, Tolios, A, Fares Taie, S, Sopic, M, Rajdl, D, et al.. On behalf of the DGKL working group “Digital Competence”. Digital competence in laboratory medicine. J Lab Med 2023;47:143–8. https://doi.org/10.1515/labmed-2023-0021.
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