Data decomposition and dual clustering for clinical care management

This paper proposes a method for construction of a clinical pathway based on attribute and sample clustering, called dual clustering. The method consists of the following five steps: first, histories of nursing orders are extracted from hospital information system. Second, orders are classified into several groups by using clustering on the pricipal components (sample clustering). Third, attributes clustering is applied to the data. Fourth, the method compares between generated functions for sample and attribute clustering which relate the number of clusters and calculated similarities. Fifth, if attribute clustering gives better performance with respect to the function, the dataset is decomposed into subtables by using the grouping of attribute clustering. Then, the first step will be repeated in a recursive way. After the grouping results are stable, a new pathway will be constructed from all the induced results. The method was applied to datasets of a disease extracted from a hospital information system. The results show that the proposed method is useful for construction of a clinical pathway.