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EHRtemporalVariability: delineating temporal data-set shifts in electronic health records.

Carlos Sáez1,2, Alba Gutiérrez-Sacristán2, Isaac Kohane2

  • 1Biomedical Data Science Lab, Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politècnica de València, Camino de Vera s/n, Valencia 46022, España.

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PubMed
Summary

Temporal variability in electronic health records (EHRs) can cause data shifts, harming research. The EHRtemporalVariability R package helps identify these shifts, ensuring reliable EHR data reuse for biomedical studies.

Keywords:
R packageclaims datadata qualitydata-set shiftselectronic health recordsinformation geometryresearch repositoriesscientific data setstemporal variabilityvisual analytics

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Area of Science:

  • Biomedical Informatics
  • Data Science
  • Health Informatics

Background:

  • Healthcare processes exhibit inherent temporal variability, potentially causing dataset shifts in electronic health records (EHRs).
  • These shifts, appearing as trends or abrupt changes, complicate the reuse of multimodal and coded EHR data for research.
  • Failure to identify temporal dataset shifts can negatively impact population health studies and machine learning applications.

Purpose of the Study:

  • To introduce EHRtemporalVariability, an open-source R package and Shiny app.
  • To provide tools for exploring and identifying temporal dataset shifts in EHR data.
  • To facilitate reliable data reuse in biomedical research.

Main Methods:

  • Estimates statistical distributions of coded and numerical data over time.
  • Projects temporal evolution using non-parametric information geometric plots.
  • Enables exploration of variable changes via data temporal heat maps.

Main Results:

  • The EHRtemporalVariability package successfully delineates dataset shifts in EHR data.
  • Demonstrated utility through three impact case studies, with one offering reproducibility.
  • The software is accessible via an R package and a user-friendly Shiny application.

Conclusions:

  • EHRtemporalVariability aids in the exploration and identification of dataset shifts.
  • Contributes to the effective examination and repurposing of large, longitudinal EHR datasets.
  • Ensures reliable data reuse for diverse biomedical data users, including technical and non-technical users.