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.
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.
Authors: John W Ely; Jerome A Osheroff; M Lee Chambliss; Mark H Ebell; Marcy E Rosenbaum Journal: J Am Med Inform Assoc Date: 2004-11-23 Impact factor: 4.497
Authors: P D Clayton; S P Narus; S M Huff; T A Pryor; P J Haug; T Larkin; S Matney; R S Evans; B H Rocha; W A Bowes; F T Holston; M L Gundersen Journal: Methods Inf Med Date: 2003 Impact factor: 2.176
Authors: Guilherme Del Fiol; Peter J Haug; James J Cimino; Scott P Narus; Chuck Norlin; Joyce A Mitchell Journal: J Am Med Inform Assoc Date: 2008-08-28 Impact factor: 4.497