A comparison of patient classification using data mining in acute health care

Abstract

Patients’ diagnoses are used currently as a basis for resource consumption. There are other alternative forms of groupings: one approach is to group patients according to common characteristics and infer their resource consumption based on their group membership. In this paper, we compare the effectiveness of the alternative forms of patient classification obtained from data mining with the current classification for an objective assessment of the average difference between the inferred and the actual resource consumption. In tackling this prediction tasks, classification trees and neural clustering are used. Demographic and hospital admission information is used to generate the clusters and..