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Review
. 2022 Dec:134:102431.
doi: 10.1016/j.artmed.2022.102431. Epub 2022 Oct 22.

Ubiquitous and smart healthcare monitoring frameworks based on machine learning: A comprehensive review

Anand Motwani  1 , Piyush Kumar Shukla  2 , Mahesh Pawar  3
Affiliations
Review

Ubiquitous and smart healthcare monitoring frameworks based on machine learning: A comprehensive review

Anand Motwani et al. Artif Intell Med. 2022 Dec.

Abstract

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.

Keywords: Big data; Chronic diseases; Cloud computing; Cognitive computing; Data analytics; Edge computing; Internet-of-things; Machine learning; Remote patient monitoring; Smart healthcare monitoring; Ubiquitous computing.

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Conflict of interest statement

Declaration of competing interest The authors have no conflict of interest to disclose. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Figures

Fig. 1
Fig. 1
Healthcare informatics use-cases.
Fig. 2
Fig. 2
IoMT at a glance.
Fig. 3
Fig. 3
Flow for study search and selection strategy based on the PRISMA.
Fig. 4
Fig. 4
Number of studies based on publication venue.
Fig. 5
Fig. 5
Illustration of smart and ubiquitous healthcare monitoring framework.
Fig. 6
Fig. 6
Classification of major components of SHM.
Fig. 7
Fig. 7
Categorization of technical challenges in SHM frameworks.
Fig. 8
Fig. 8
Taxonomy of research articles surveyed.
Fig. 9
Fig. 9
ML implementation challenges in SHM frameworks.
See this image and copyright information in PMC

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