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Related Experiment Video

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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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Visualizing the data - using lifelines2 to gain insights from data drawn from a clinical data repository.

John D Manning1, Beatriz E Marciano, James J Cimino

  • 1Carilion Clinic / Virginia Tech Carilion, Roanoke, VA;

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|December 5, 2013
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Summary

Visual analytics tools like Lifelines2 enable rapid interpretation of electronic health record (EHR) data. This study used Lifelines2 to analyze inflammation markers in patients with and without chronic granulomatous disease (CGD).

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Area of Science:

  • Health Informatics
  • Biomedical Data Visualization
  • Clinical Research Informatics

Background:

  • Visual analytics is increasingly applied to electronic health record (EHR) analysis.
  • Lifelines2, a visualization program, was integrated into the National Institutes of Health (NIH) clinical repository (BTRIS).
  • EHR data analysis requires effective tools for interpreting complex patient information.

Purpose of the Study:

  • To explore the functionality of Lifelines2 for EHR data analysis.
  • To compare inflammation markers (ESR and CRP) in patients with and without chronic granulomatous disease (CGD).
  • To assess the utility of visual analytics in identifying patterns within large patient datasets.

Main Methods:

  • Utilized Lifelines2 visualization software for EHR data analysis.
  • Analyzed ten years of de-identified patient data, focusing on 622 patients and 12,266 laboratory events.
  • Compared erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) levels in patients with and without chronic granulomatous disease (CGD).

Main Results:

  • Lifelines2 facilitated the visualization of correlations among laboratory events.
  • The analysis identified patterns in inflammation markers related to chronic granulomatous disease (CGD).
  • Visualizations enabled rapid and powerful interpretation of complex EHR data.

Conclusions:

  • Visual analytics tools like Lifelines2 are effective for interpreting EHR data.
  • The findings suggest areas for further research on specific patient population subsets.
  • Lifelines2 aids in understanding disease markers and patient cohorts within clinical repositories.