Use of classification models based on usage data for the selection of infobutton resources.
UNLABELLED: "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. CONCLUSION: The application of data mining tools over infobutton usage data is a promising strategy to further improve the prediction capability of infobuttons.
Start / End Page
Pubmed Central ID
Electronic International Standard Serial Number (EISSN)