Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System

Almujally, Nouf; Aljrees, Turki; Saidani, Oumaima; Umer, Muhammad; Faheem, Zaid Bin; Abuzinadah, Nihal; Alnowaiser, Khaled; Ashraf, Imran

doi:10.3390/s23104580

Cited by 14 publications

(5 citation statements)

References 64 publications

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“…The SMOTE method is applied to both the ETC and the CNN models. The outcomes presented in Table 6 demonstrate that the CNN model performed better than ETC and CNN models employed with SMOTE in the study 85 and DL model in 86 .…”

Section: Resultsmentioning

confidence: 89%

Heart failure patients monitoring using IoT-based remote monitoring system

Umer,

Aljrees,

Karamti

et al. 2023

Sci Rep

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Intelligent health monitoring systems are becoming more important and popular as technology advances. Nowadays, online services are replacing physical infrastructure in several domains including medical services as well. The COVID-19 pandemic has also changed the way medical services are delivered. Intelligent appliances, smart homes, and smart medical systems are some of the emerging concepts. The Internet of Things (IoT) has changed the way communication occurs alongside data collection sources aided by smart sensors. It also has deployed artificial intelligence (AI) methods for better decision-making provided by efficient data collection, storage, retrieval, and data management. This research employs health monitoring systems for heart patients using IoT and AI-based solutions. Activities of heart patients are monitored and reported using the IoT system. For heart disease prediction, an ensemble model ET-CNN is presented which provides an accuracy score of 0.9524. The investigative data related to this system is very encouraging in real-time reporting and classifying heart patients with great accuracy.

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Section: Resultsmentioning

confidence: 89%

Heart failure patients monitoring using IoT-based remote monitoring system

Umer,

Aljrees,

Karamti

et al. 2023

Sci Rep

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“…In addition, it uses artificial intelligence (AI) approaches to control a huge amount of data, which leads to better storage, management, use, and decision making. The created system monitors the clothing activities of patients, which helps to inform patients about their health status [ 105 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

Stamate,

Piraianu,

Ciobotaru

et al. 2024

Diagnostics

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Background: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. Results: We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. Conclusions: The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.

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“…This platform allows users to connect to the IoT backbone and collect data on equipment's health monitoring, indoor climate, miniaturization, manufacturing process automation, etc. [34]. Employee's Experience: The employee experience layer is created to benefit employees by monitoring equipment health and identifying temperature, humidity, air, pressure, and moisture-based indoor climate change.…”

Section: Iiot Network Platform Based On Big Data Analyticsmentioning

confidence: 99%

A Survey on the Role of Industrial IoT in Manufacturing for Implementation of Smart Industry

Farooq,

Abdullah,

Riaz

et al. 2023

Sensors

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The Internet of Things (IoT) is an innovative technology that presents effective and attractive solutions to revolutionize various domains. Numerous solutions based on the IoT have been designed to automate industries, manufacturing units, and production houses to mitigate human involvement in hazardous operations. Owing to the large number of publications in the IoT paradigm, in particular those focusing on industrial IoT (IIoT), a comprehensive survey is significantly important to provide insights into recent developments. This survey presents the workings of the IoT-based smart industry and its major components and proposes the state-of-the-art network infrastructure, including structured layers of IIoT architecture, IIoT network topologies, protocols, and devices. Furthermore, the relationship between IoT-based industries and key technologies is analyzed, including big data storage, cloud computing, and data analytics. A detailed discussion of IIoT-based application domains, smartphone application solutions, and sensor- and device-based IIoT applications developed for the management of the smart industry is also presented. Consequently, IIoT-based security attacks and their relevant countermeasures are highlighted. By analyzing the essential components, their security risks, and available solutions, future research directions regarding the implementation of IIoT are outlined. Finally, a comprehensive discussion of open research challenges and issues related to the smart industry is also presented.

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Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System

Cited by 14 publications

References 64 publications

Heart failure patients monitoring using IoT-based remote monitoring system

Heart failure patients monitoring using IoT-based remote monitoring system

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

A Survey on the Role of Industrial IoT in Manufacturing for Implementation of Smart Industry

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