Improving the quality of patient care using reliability measures: a classification tree approach.
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.
Graham
P L
PL
CSIRO Mathematical and Information Sciences, North Ryde NSW 2113, Australia. petra.graham@csiro.au
Kuhnert
P M
PM
Cook
D A
DA
Mengersen
K
K
D001699:Biometry; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D006801:Humans; D009026:Mortality; D017063:Outcome Assessment, Health Care; D005791:Patient Care / Q000592:standards* / Q000706:statistics & numerical data; D011336:Probability; D011787:Quality of Health Care / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D018570:Risk Assessment