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Visual Analytics in Delirium Management.

Werner O Hackl1, Michael Netzer1, Renate Nantschev1

  • 1UMIT - Private University for Health Sciences, Medical Informatics and Technology, Institute of Medical Informatics, Hall in Tirol, Austria.

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|May 9, 2021
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Summary
This summary is machine-generated.

This study visualizes delirium in elderly patients using heatmaps and dendrograms. These data mining techniques help track delirium over time in gerontopsychiatric care.

Keywords:
Delirium managementdata visualizationpatient safety

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Area of Science:

  • Geriatric Medicine
  • Data Science
  • Clinical Informatics

Background:

  • Delirium is a significant patient safety concern in the elderly population.
  • Characterized by fluctuating progression, delirium requires effective monitoring methods.
  • Gerontopsychiatric settings present unique challenges for delirium management.

Purpose of the Study:

  • To identify and evaluate data mining visualization techniques for studying delirium.
  • To develop methods for visualizing delirium occurrence over patient stays.
  • To enhance the understanding of delirium's temporal dynamics in gerontopsychiatric care.

Main Methods:

  • Analysis of existing data mining visualization techniques.
  • Application to a delirium dataset from a gerontopsychiatric setting.
  • Utilizing hierarchical clustering for data analysis.

Main Results:

  • Heatmaps emerged as a suitable visualization method for delirium data.
  • Dendrograms from hierarchical clustering also proved effective.
  • These methods allow for the visualization of delirium patterns over time.

Conclusions:

  • Heatmaps combined with hierarchical clustering are effective data mining tools.
  • This visualization technique aids in studying delirium cases within patient stays.
  • The findings support improved monitoring and management of delirium in clinical practice.