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Use of classification models based on usage data for the selection of infobutton resources
Guilherme Del Fiol1, Peter J Haug
1University of Utah, Salt Lake City, UT, USA.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|August 13, 2008
Summary
Data mining enhances infobuttons, improving clinical information retrieval. New models predict user resource selection more accurately than current systems.
Area of Science:
- Medical Informatics
- Health Services Research
- Data Science
Background:
- Infobuttons are tools that predict clinician information needs within a specific context.
- Effective information retrieval is crucial for evidence-based clinical decision-making.
Purpose of the Study:
- To develop classification models using infobutton usage data.
- To predict the most likely information resource selected by a user.
Main Methods:
- Applied data mining techniques to 7,968 infobutton sessions over six months.
- Utilized a dataset with 13 attributes.
- Generated and compared five distinct classification models.
Main Results:
- All developed models demonstrated statistically significant improvements over the existing institutional implementation.
- Optimal prediction performance was achieved using only two to five attributes.
- The models accurately predicted user information resource selection.
Conclusions:
- Data mining of infobutton usage data offers a promising approach to enhance prediction capabilities.
- Improved infobutton functionality can lead to more efficient clinical information access.
- This strategy supports the advancement of clinical decision support systems.
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