文章基本信息

标题：A Self-Adaptive and Efficient Context-Aware Healthcare Model for COPD Diseases
本地全文：下载
作者：Hamid Mcheick ; John Sayegh
期刊名称：Informatics
电子版ISSN：2227-9709
出版年度：2021
卷号：8
期号：3
页码：41
DOI：10.3390/informatics8030041
出版社：MDPI Publishing
摘要：The emergence of pervasive computing technology has revolutionized all aspects of life and facilitated many everyday tasks. As the world fights the coronavirus pandemic, it is necessary to find new ways to use technology to fight diseases and reduce their economic burden. Distributed systems have demonstrated efficiency in the healthcare domain, not only by organizing and managing patient data but also by helping doctors and other medical experts to diagnose diseases and take measures to prevent the development of serious conditions. In the case of chronic diseases, telemonitoring systems provide a way to monitor patients’ states and biomarkers in the course of their everyday routines. We developed a Chronical Obstructive Pulmonary Disease (COPD) healthcare system to protect patients against risk factors. However, each change in the patient context initiated the execution of the system’s entire rule base, which diminished performance. In this article, we use separation of concerns to reduce the impact of contextual changes by dividing the context, rules and services into software modules (units). We combine healthcare telemonitoring with context awareness and self-adaptation to create an adaptive architecture model for COPD patients. The model’s performance is validated using COPD data, demonstrating the efficiency of the separation of concerns and adaptation techniques in context-aware systems.
关键词：software architecture; self-adaptation; context-aware system; COPD; separation of concerns; healthcare systems software architecture ; self-adaptation ; context-aware system ; COPD ; separation of concerns ; healthcare systems