School of Computing Science & Engineering, VIT Bhopal University, Sehore, (MP) 466114, India; Department of Computer Science & Engineering, University Institute of Technology, RGPV, Bhopal, (MP) 462033, India.
Department of Computer Science & Engineering, University Institute of Technology, RGPV, Bhopal, (MP) 462033, India.
Artif Intell Med. 2022 Dec;134:102431. doi: 10.1016/j.artmed.2022.102431. Epub 2022 Oct 22.
During the COVID-19 pandemic, the patient care delivery paradigm rapidly shifted to remote technological solutions. Rising rates of life expectancy of older people, and deaths due to chronic diseases (CDs) such as cancer, diabetes and respiratory disease pose many challenges to healthcare. While the feasibility of Remote Patient Monitoring (RPM) with a Smart Healthcare Monitoring (SHM) framework was somewhat questionable before the COVID-19 pandemic, it is now a proven commodity and is on its way to becoming ubiquitous. More health organizations are adopting RPM to enable CD management in the absence of individual monitoring. The current studies on SHM have reviewed the applications of IoT and/or Machine Learning (ML) in the domain, their architecture, security, privacy and other network related issues. However, no study has analyzed the AI and ubiquitous computing advances in SHM frameworks. The objective of this research is to identify and map key technical concepts in the SHM framework. In this context an interesting and meaningful classification of the research articles surveyed for this work is presented. The comprehensive and systematic review is based on the "Preferred Reporting Items for Systematic Review and Meta-Analysis" (PRISMA) approach. A total of 2540 papers were screened from leading research archives from 2016 to March 2021, and finally, 50 articles were selected for review. The major advantages, developments, distinctive architectural structure, components, technical challenges and possibilities in SHM are briefly discussed. A review of various recent cloud and fog computing based architectures, major ML implementation challenges, prospects and future trends is also presented. The survey primarily encourages the data driven predictive analytics aspects of healthcare and the development of ML models for health empowerment.
在 COVID-19 大流行期间，患者护理模式迅速向远程技术解决方案转变。老年人预期寿命的上升率，以及癌症、糖尿病和呼吸道疾病等慢性病（CDs）导致的死亡，给医疗保健带来了许多挑战。虽然在 COVID-19 大流行之前，远程患者监测（RPM）与智能医疗监测（SHM）框架的可行性有些值得怀疑，但现在它已成为一种经过验证的商品，并正在普及。越来越多的健康组织正在采用 RPM 来实现 CD 管理，而无需进行个体监测。目前关于 SHM 的研究已经审查了物联网和/或机器学习（ML）在该领域的应用、它们的架构、安全性、隐私和其他网络相关问题。然而，没有研究分析过 SHM 框架中的人工智能和普及计算进展。本研究的目的是确定和绘制 SHM 框架中的关键技术概念。在这种情况下，提出了对这项工作进行调查的研究文章的一个有趣且有意义的分类。全面而系统的审查是基于"系统评价和荟萃分析的首选报告项目"（PRISMA）方法。从 2016 年到 2021 年 3 月，从主要研究档案中筛选出 2540 篇论文，最终选择了 50 篇进行审查。简要讨论了 SHM 的主要优势、发展、独特的架构结构、组件、技术挑战和可能性。还介绍了各种基于云和雾计算的最新架构、主要 ML 实施挑战、前景和未来趋势的回顾。该调查主要鼓励医疗保健方面的数据驱动预测分析以及用于健康赋权的 ML 模型的开发。
