annotable_strings

<p>A dataset containing commonly used strings in annotations.</p>

datasets

Navigating the shift of clinical laboratory data from primary everyday clinical use to secondary research purposes presents a significant challenge. Given the substantial time and expertise required for lab data pre-processing and cleaning and the lack of all-in-one tools tailored for this need, we developed our algorithm 'lab2clean' as an open-source R-package. 'lab2clean' package is set to automate and standardize the intricate process of cleaning clinical laboratory results. With a keen focus on improving the data quality of laboratory result values and units, our goal is to equip researchers with a straightforward, plug-and-play tool, making it smoother for them to unlock the true potential of clinical laboratory data in clinical research and clinical machine learning (ML) model development. Functions to clean & validate result values (Version 1.0) are described in detail in 'Zayed et al. (2024)' <doi:10.1186/s12911-024-02652-7>. Functions to standardize & harmonize result units (added in Version 2.0) are described in detail in 'Zayed et al. (2025)' <doi:10.1016/j.ijmedinf.2025.106131>.

Ahmed Zayed

lab2clean

Automation and Standardization of Cleaning Clinical Laboratory
Data

Ilias Sarikakis

Arne Janssens

Pavlos Mamouris

annotable_strings function

<p>A data frame with 2679 rows and 1 variable.</p>

Format

Annotable Strings for Unit Standardization — annotable_strings

Annotable Strings for Unit Standardization

annotable_strings: Annotable Strings for Unit Standardization

Description

Usage

Arguments

Format

Details