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Contrasting temporal trend discovery for large healthcare databases.

Goran Hrovat1, Gregor Stiglic, Peter Kokol

  • 1Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia.

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Summary

This study introduces a novel data mining approach for analyzing electronic health records, revealing hidden temporal trends in patient subgroups. The method uncovers opposite trends by age and sex, aiding healthcare decision-making.

Keywords:
Data miningDecision supportTrend discovery

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Area of Science:

  • Health Informatics
  • Data Mining
  • Healthcare Analytics

Background:

  • Increasing adoption of electronic health records (EHRs) drives demand for advanced data analysis techniques.
  • Traditional methods struggle to identify complex temporal trends within diverse patient populations.

Purpose of the Study:

  • To introduce a novel data mining approach for exploring and comparing temporal trends in in-patient subgroups.
  • To leverage visual analytics and associated rule mining for enhanced big data exploration.

Main Methods:

  • Utilized the Apriori algorithm and linear model-based recursive partitioning for associated rule mining.
  • Employed visual analytics, including regression trees with scatter plots and trend lines, for big data trend discovery.
  • Evaluated the approach using the Nationwide Inpatient Sample (NIS) database.

Main Results:

  • Demonstrated the discovery of opposite temporal trends in age and sex-based patient subgroups.
  • Highlighted the inability of traditional trend-tracking techniques to identify these nuanced trends.
  • Showcased the effectiveness of the proposed visual analytics approach for big data trend analysis.

Conclusions:

  • The novel approach effectively uncovers complex temporal trends in healthcare data, particularly those missed by conventional methods.
  • This technique offers valuable decision support for policymakers and hospital management in identifying and responding to critical health trends.
  • Visual analytics integrated with data mining provides a powerful tool for understanding patient subgroup dynamics over time.