Difference between revisions of "Automated development of order sets and corollary orders by data mining in an ambulatory computerized physician order entry system"
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Wright, A., & Sittig, D. F. (2006). Automated Development of Order Sets and Corollary Orders by Data Mining in an Ambulatory Computerized Physician Order Entry System. AMIA Annual Symposium Proceedings, 2006, 819–823.<ref name= "OrderSets">Wright, A., & Sittig, D. F. (2006). Automated Development of Order Sets and Corollary Orders by Data Mining in an Ambulatory Computerized Physician Order Entry System. AMIA Annual Symposium Proceedings, 2006, 819–823 http://www-ncbi-nlm-nih-gov.ezproxyhost.library.tmc.edu/pmc/articles/PMC1839652/</ref> | Wright, A., & Sittig, D. F. (2006). Automated Development of Order Sets and Corollary Orders by Data Mining in an Ambulatory Computerized Physician Order Entry System. AMIA Annual Symposium Proceedings, 2006, 819–823.<ref name= "OrderSets">Wright, A., & Sittig, D. F. (2006). Automated Development of Order Sets and Corollary Orders by Data Mining in an Ambulatory Computerized Physician Order Entry System. AMIA Annual Symposium Proceedings, 2006, 819–823 http://www-ncbi-nlm-nih-gov.ezproxyhost.library.tmc.edu/pmc/articles/PMC1839652/</ref> | ||
| + | == Introduction == | ||
| + | This article presents two alternate techniques to create order sets and corollary orders utilizing data mining of past ordering behavior. Order sets are collections of related items which are commonly ordered together; typically used to treat specific clinical conditions. Corollary orders are orders triggered as a consequence of another order. | ||
| + | == Method == | ||
| + | |||
| + | Two complementary data mining techniques were used: | ||
| + | |||
| + | 1. Frequent itemset mining (market basket analysis) | ||
| + | |||
| + | 2. Association rule mining | ||
Revision as of 07:28, 12 November 2015
Article Review
Wright, A., & Sittig, D. F. (2006). Automated Development of Order Sets and Corollary Orders by Data Mining in an Ambulatory Computerized Physician Order Entry System. AMIA Annual Symposium Proceedings, 2006, 819–823.[1]
Introduction
This article presents two alternate techniques to create order sets and corollary orders utilizing data mining of past ordering behavior. Order sets are collections of related items which are commonly ordered together; typically used to treat specific clinical conditions. Corollary orders are orders triggered as a consequence of another order.
Method
Two complementary data mining techniques were used:
1. Frequent itemset mining (market basket analysis)
2. Association rule mining
References
- ↑ Wright, A., & Sittig, D. F. (2006). Automated Development of Order Sets and Corollary Orders by Data Mining in an Ambulatory Computerized Physician Order Entry System. AMIA Annual Symposium Proceedings, 2006, 819–823 http://www-ncbi-nlm-nih-gov.ezproxyhost.library.tmc.edu/pmc/articles/PMC1839652/
