Mobile healthcare (m‐Health) based on artificial intelligence in healthcare 4.0

Abstract

Healthcare 4.0 is about collecting huge amounts of data and getting it to work in applications, enabling healthcare management decisions well‐informed while providing for important gains in effectiveness and cost control. Diagnostics based on the digital footprint depend on wearable technology's ability to gather and extract essential patient data. Artificial intelligence (AI) technologies allow the analysis of real‐time observed data and continuously developing from data to understand the world surrounding them. To connect and access intelligent healthcare services, people, and devices at any time, a secure wireless mobile communication system is essential. This article suggests a mHealth‐based patient monitoring system (mHealth‐PMS) based on AI for healthcare 4.0. Mobile healthcare applications motorized by Convolutional Neural Network (CNN) have enabled people to triage their conditions and preemptive treatment decisions. Information collected has been analysed for substantiating cause, and alert and preventive messages have been immediately sent through the mobile application. The performance analysis has been executed, and the proposed mobile application‐based surveillance provided much‐enhanced reporting of information quickly on diseases, symptoms, factors, and more. The mHealth‐PMS strategy shows an accuracy ratio of 95.6%, monitoring ratio of 93.5%, data management ratio of 94.4%, data security ratio of 91.7%, data privacy ratio of 92.1%, prediction ratio of 95.3%, a cost‐effective ratio of 25.5% compared to the existing methods.

Biocompatible Electronic Skins for Cardiovascular Health Monitoring

“…[256] In 2022, Sharma et al proposed a mHealth-based patient monitoring system (mHealth-PMS) with a mobile healthcare application powered by CNN that enables users to classify their conditions and make treatment judgments with a significant breakthrough in decision-making efficiency compared to the previous ones (Figure 11b). [257] Apart from that, smart healthcare platforms connected by IoT technologies are on the rise (Figure 11c,d). Devices connected by IoT can connect with many elements from different domains to share information and resources without the constraints of time and distance.…”

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

“…In smart healthcare, significant operations are automated using e‐health systems based on intelligent IoT (Sharma, Al‐Wanain, et al (2022), Nepal et al (2015), Nayak et al (2022)). Scientists have established a vast range of functional intelligent devices that track patients' conditions of health on the whole, as shown in Figure 7.…”

“…For instance, smart e‐health systems help senior citizens and disabled people cope with their mobility. It is possible to use IoT devices to distantly capture and relay this information to significant health data collection and diagnostic centers (e.g., cardiovascular, blood pressure, and anatomy data) (Luong et al (2016), Sharma, Al‐Wanain, et al (2022)). The most critical risks to healthcare facilities are data protection and privacy of the patient data collected for medical purposes (Djenouri et al (2022), Tariq, Asim, and Al‐Obeidat (2019)).…”

BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria

“…With the expansion in volume and complexity of biological data, machine learning (ML) algorithms have been successfully applied to their analysis [ 3–5 ]. ML algorithms can extract new and useful knowledge from biological data [ 6 ], allowing complex analyses, speeding up new findings and reducing research costs [ 7 ]. These advances bring important social and economical benefits, such as improving diagnosis, treatment and the design of new medications [ 7–9 ], e.g.…”

“…ML algorithms can extract new and useful knowledge from biological data [ 6 ], allowing complex analyses, speeding up new findings and reducing research costs [ 7 ]. These advances bring important social and economical benefits, such as improving diagnosis, treatment and the design of new medications [ 7–9 ], e.g. coronavirus disease 2019 [ 8 , 10 ], cancer diagnosis [ 11 ] and CRISPR/Cas9-based gene-editing technology [ 12 , 13 ].…”