Mining Schedule of Nursing Care Based on Dual-Clustering

Abstract

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 four 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. Finally, original temporal data are split into several sub-tables and 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 constructed a clinical pathway, which was not only similar to the pathway manually acquired from medical experts but also discovered nursing orders which they forget to include.

Türki̇ye Şehi̇rleri̇ni̇n Sosyo-Ekonomi̇k Özelli̇kleri̇ni̇n Bölgesel Anali̇zi̇

“…A fuzzy clustering approach is used by Ben-Arieh and Gullipalli (2012) through data envelopment analysis with spars input and output data. Moreover, Tsumoto et al (2015) propose a method for the construction of a clinical pathway based on attribute and sample clustering, called dual clustering. Within the same cluster, the similarity among samples is higher, and the dissimilarity among samples in different clusters is higher.…”