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An automated data cleaning method for Electronic Health Records by incorporating clinical knowledge by Shi, Xi (author);Prins, Charlotte (author);Van Pottelbergh, Gijs (author);Mamouris, Pavlos (author);Vaes, Bert (author);De Moor, Bart (author) is a Health Professions article available to read on EtoBox.

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Abstract Background The use of Electronic Health Records (EHR) data in clinical research is incredibly increasing, but the abundancy of data resources raises the challenge of data cleaning. It can save time if the data cleaning can be done automatically. In addition, the automated data cleaning tools for data in other domains often process all variables uniformly, meaning that they cannot serve well for clinical data, as there is variable-specific information that needs to be considered. This paper proposes an automated data cleaning method for EHR data with clinical knowledge taken into consideration. Methods We used EHR data collected from primary care in Flanders, Belgium during 1994–2015. We constructed a Clinical Knowledge Database to store all the variable-specific information that is necessary for data cleaning. We applied Fuzzy search to automatically detect and replace the wrongly spelled units, and performed the unit conversion following the variable-specific conversion formula. Then the numeric values were corrected and outliers were detected considering the clinical knowledge. In total, 52 clinical variables were cleaned, and the percentage of missing values (completene

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Author

Shi, Xi (author);Prins, Charlotte (author);Van Pottelbergh, Gijs (author);Mamouris, Pavlos (author);Vaes, Bert (author);De Moor, Bart (author)

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Health Professions (Health Sciences)