Use of classification models based on usage data for the selection of infobutton resources.
"Infobuttons" are information retrieval tools that predict the questions and the on-line information resources that a clinician may need in a particular context. The goal of this study was to employ infobutton usage data to produce classification models that predict the information resource that is most likely to be selected by a user in a given context.


METHODS
Data mining techniques were applied to a dataset with 13 attributes and 7,968 infobutton sessions conducted in a six-month period. Five classification models were generated and compared in terms of prediction performance.


RESULTS
All classification models performed statistically better than the implementation currently in use at our institution. Two to five attributes were sufficient for the models to achieve their best performance.


CONCLUSIONS
The application of data mining tools over infobutton usage data is a promising strategy to further improve the prediction capability of infobuttons.
Del Fiol
Guilherme
G
University of Utah, Salt Lake City, UT, USA.
Haug
Peter J
PJ
D001499:Bayes Theorem; D003625:Data Collection / Q000379:methods; D019992:Databases as Topic; D003663:Decision Trees; D005240:Feasibility Studies; D007255:Information Services / Q000706:statistics & numerical data; D016247:Information Storage and Retrieval / Q000706:statistics & numerical data; D015233:Models, Statistical; D009862:Online Systems / Q000706:statistics & numerical data*; D014584:User-Computer Interface