Development of Pain Prescription Decision Systems for Nursing Intervention

Abstract

In modern society, the amount of information has significantly increased due to the development of the Internet and IT convergence technology. This leads to developing information obtaining and searching technologies from much data. Although system integration for u-healthcare has been largely established to accumulate large amounts of information, there is a lack of provision and support of information for nursing activities, using such an established database. In particular, the judgment for pain intervention depends on the experience of individual nurses, leading to usually making subjective decisions. Thus, there is some danger in applying unwanted anesthesia and drug abuse. In this paper, we proposed the development of the pain prescription decision systems for the nursing intervention. The applied collaborative filtering is a method that extracts some items, which represent a high relative level, based on similar preferences. A preference estimation method using a user based collaborative filtering calculates user similarities through Pearson correlation coefficients in which a neighbor selection method is used based on the user preference for items. In general, medical data in patients shows various distributions due to its own characteristics, as sample data demonstrates. Therefore, this is determined as an applicable theory to the sparsity problem. In addition, it is compensated using a default voting method. Field data evaluated by applying standard data and its accuracy in pain prediction is verified. The test of the proposed method yielded excellent extraction results. It is possible to provide the fundamental data and guideline to nurses for recognizing the pain of patients based on the results of this study. This represents increased patient welfare. Ultimately, this paper suggests empirical application to verify the adequacy and the validity with the proposed systems. Accordingly, the satisfaction and the quality of services will be improved the nursing intervention.