Protocol for EHR laboratory data preprocessing and seasonal adjustment using R and RStudio

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Protocol for EHR laboratory data preprocessing and seasonal adjustment using R and RStudio. / Muse, Victorine P.; Brunak, Søren.

In: STAR Protocols, Vol. 5, No. 1, 102912, 2024.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Muse, VP & Brunak, S 2024, 'Protocol for EHR laboratory data preprocessing and seasonal adjustment using R and RStudio', STAR Protocols, vol. 5, no. 1, 102912. https://doi.org/10.1016/j.xpro.2024.102912

APA

Muse, V. P., & Brunak, S. (2024). Protocol for EHR laboratory data preprocessing and seasonal adjustment using R and RStudio. STAR Protocols, 5(1), [102912]. https://doi.org/10.1016/j.xpro.2024.102912

Vancouver

Muse VP, Brunak S. Protocol for EHR laboratory data preprocessing and seasonal adjustment using R and RStudio. STAR Protocols. 2024;5(1). 102912. https://doi.org/10.1016/j.xpro.2024.102912

Author

Muse, Victorine P. ; Brunak, Søren. / Protocol for EHR laboratory data preprocessing and seasonal adjustment using R and RStudio. In: STAR Protocols. 2024 ; Vol. 5, No. 1.

Bibtex

@article{ac552e44a8e64adbaf13a6c2145a55fc,
title = "Protocol for EHR laboratory data preprocessing and seasonal adjustment using R and RStudio",
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",
keywords = "Bioinformatics, Health Sciences, Systems biology",
author = "Muse, {Victorine P.} and S{\o}ren Brunak",
note = "Publisher Copyright: {\textcopyright} 2024 The Author(s)",
year = "2024",
doi = "10.1016/j.xpro.2024.102912",
language = "English",
volume = "5",
journal = "STAR Protocols",
issn = "2666-1667",
publisher = "Cell Press",
number = "1",

}

RIS

TY - JOUR

T1 - Protocol for EHR laboratory data preprocessing and seasonal adjustment using R and RStudio

AU - Muse, Victorine P.

AU - Brunak, Søren

N1 - Publisher Copyright: © 2024 The Author(s)

PY - 2024

Y1 - 2024

N2 - 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

AB - 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

KW - Bioinformatics

KW - Health Sciences

KW - Systems biology

U2 - 10.1016/j.xpro.2024.102912

DO - 10.1016/j.xpro.2024.102912

M3 - Journal article

C2 - 38427569

AN - SCOPUS:85188201367

VL - 5

JO - STAR Protocols

JF - STAR Protocols

SN - 2666-1667

IS - 1

M1 - 102912

ER -

ID: 387833553