Empirical assessment of alternative methods for identifying seasonality in observational healthcare data.

ABSTRACT:

Background

Seasonality classification is a well-known and important part of time series analysis. Understanding the seasonality of a biological event can contribute to an improved understanding of its causes and help guide appropriate responses. Observational data, however, are not comprised of biological events, but timestamped diagnosis codes the combination of which (along with additional requirements) are used as proxies for biological events. As there exist different methods for determining the seasonality of a time series, it is necessary to know if these methods exhibit concordance. In this study we seek to determine the concordance of these methods by applying them to time series derived from diagnosis codes in observational data residing in databases that vary in size, typ

SUBMITTER: Molinaro A 

PROVIDER: S-EPMC9250712 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature