Health record hiccups-5,526 real-world time series with change points labelled by crowdsourced visual inspection.

ABSTRACT:

Background

Large routinely collected data such as electronic health records (EHRs) are increasingly used in research, but the statistical methods and processes used to check such data for temporal data quality issues have not moved beyond manual, ad hoc production and visual inspection of graphs. With the prospect of EHR data being used for disease surveillance via automated pipelines and public-facing dashboards, automation of data quality checks will become increasingly valuable.

Findings

We generated 5,526 time series from 8 different EHR datasets and engaged >2,000 citizen-science volunteers to label the locations of all suspicious-looking change points in the resulting graphs. Consensus labels were produced using density-based clustering with noise, with validation cond

SUBMITTER: Quan TP 

PROVIDER: S-EPMC10375518 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature