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  "Package": "lab2clean",
  "Title": "Automation and Standardization of Cleaning Clinical Laboratory\nData",
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  "Authors@R": "c(person(\"Ahmed\", \"Zayed\", , \"ahmed.zayed@kuleuven.be\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-7797-1655\")),\nperson(\"Ilias\", \"Sarikakis\", , \"sarikakisilias@gmail.com\", role = c(\"aut\", \"ctb\")),\nperson(\"Arne\", \"Janssens\", , \"arne.janssens@kuleuven.be\", role = c(\"aut\", \"ctb\")),\nperson(\"Pavlos\", \"Mamouris\", , \"pavlos.mamouris@kuleuven.be\", role = c(\"ctb\")))",
  "Description": "Navigating the shift of clinical laboratory data from\nprimary everyday clinical use to secondary research purposes\npresents a significant challenge. Given the substantial time\nand expertise required for lab data pre-processing and cleaning\nand the lack of all-in-one tools tailored for this need, we\ndeveloped our algorithm 'lab2clean' as an open-source\nR-package. 'lab2clean' package is set to automate and\nstandardize the intricate process of cleaning clinical\nlaboratory results. With a keen focus on improving the data\nquality of laboratory result values and units, our goal is to\nequip researchers with a straightforward, plug-and-play tool,\nmaking it smoother for them to unlock the true potential of\nclinical laboratory data in clinical research and clinical\nmachine learning (ML) model development. Functions to clean &\nvalidate result values (Version 1.0) are described in detail in\n'Zayed et al. (2024)' <doi:10.1186/s12911-024-02652-7>.\nFunctions to standardize & harmonize result units (added in\nVersion 2.0) are described in detail in 'Zayed et al. (2025)'\n<doi:10.1016/j.ijmedinf.2025.106131>.",
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  "Maintainer": "Ahmed Zayed <ahmed.zayed@kuleuven.be>",
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