Protocol for EHR laboratory data preprocessing and seasonal adjustment using R and RStudio
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Updated: Jul 1, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Victorine P Muse1, Søren Brunak2
1Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark.
Abstract:
Seasonality in laboratory healthcare data is associated with possible under- and overdiagnoses of patients in the clinic. Here, we present a protocol to analyze electronic health record data for seasonality patterns and adjust existing reference intervals for these changes using R software. We describe steps for preprocessing population-wide patient laboratory data into a single dataset. We then detail steps for defining strata, normalizing to median, and fitting data to selected functions. For complete details on the use and execution of this protocol, please refer to Muse et al. (2023).1.
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