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Improving the quality of patient care using reliability measures: a classification tree approach
Author(s) -
Graham P. L.,
Kuhnert P. M.,
Cook D. A.,
Mengersen K.
Publication year - 2006
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2461
Subject(s) - reliability (semiconductor) , computer science , identification (biology) , decision tree , tree (set theory) , data mining , quality (philosophy) , node (physics) , decision tree learning , machine learning , mathematics , engineering , mathematical analysis , power (physics) , philosophy , botany , physics , structural engineering , epistemology , quantum mechanics , biology
This paper considers the application and interpretation of new reliability measures for a classification tree‐based medical risk assessment tool. Following the construction of a classification tree reliability measures may then be used to provide an estimate of the precision of the classification and the probability in each terminal node of the classification tree. Identification of unreliable nodes (those that have low precision) in this application may indicate patient groups requiring closer monitoring or scenarios in which further information about the patient is required, thereby providing medical practitioners with an avenue for more informed decision making. Copyright © 2006 John Wiley & Sons, Ltd.