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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
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DisEpi: Compact Visualization as a Tool for Applied Epidemiological Research.

Arriel Benis1, Moshe Hoshen1

  • 1Clalit Research Institute, Chief Physician's Office, Clalit Health Services, Tel-Aviv, Israel.

Studies in Health Technology and Informatics
|October 18, 2017
PubMed
Summary

A new tool, DisEpi, visualizes healthcare data for public health discovery. It identified trends in Attention-Deficit/Hyperactivity Disorder (ADHD) medication use among children, showing younger children are treated more often and medication incidence stabilized after 2011.

Keywords:
ADHDClusteringEpidemiologyFacilitationHeatmapPublic Health InformaticsVisual Data Mining

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Area of Science:

  • Epidemiology
  • Public Health
  • Health Informatics

Background:

  • Healthcare databases are crucial for outcomes research and evidence-based medicine.
  • Modern epidemiologic studies require advanced data comprehension techniques.
  • Visual exploration tools are needed to support Public Health Knowledge Discovery.

Purpose of the Study:

  • To introduce DisEpi, a novel tool for visual exploration of epidemiological data.
  • To apply DisEpi to analyze socio-demographic and prescription data for Attention-Deficit/Hyperactivity Disorder (ADHD) in children.
  • To identify patterns, temporal changes, and population clusters in ADHD treatment data.

Main Methods:

  • DisEpi was used to analyze data from 1,605,800 children (aged 6-17) with ADHD.
  • The study analyzed prescription data between 2006 and 2016 from Clalit Health Services.
  • DisEpi combines hierarchical clustering graphics and heatmaps to visualize disease time-trends.

Main Results:

  • DisEpi enabled efficient visual analysis of ADHD data mining results.
  • Younger children, particularly the youngest in their class, received ADHD medication more frequently.
  • Medication incidence increased from 2006-2011, then stabilized, with varying progression rates across three identified patient clusters.

Conclusions:

  • DisEpi provides a user-friendly format for domain experts to analyze complex epidemiological data.
  • The tool requires minimal preparation and fewer iterations compared to classical statistical methods.
  • DisEpi facilitates efficient discovery, interpretation, and policy implementation in public health.