An Integrative Framework for Online Prognostic and Health Management Using Internet of Things and Convolutional Neural Network

Qu, Yuanju; Ming, Xinguo; Qiu, Siqi; Zheng, Maokuan; Hou, Zhufeng

doi:10.3390/s19102338

Cited by 19 publications

(9 citation statements)

References 42 publications

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“…In addition to data access, the participation of patients in providing health conditions and lifestyle data to the physicians will aid in better prognosis and diagnosis. The introduction of IoT-based health data management system involving sensors and medical devices that monitor a patient’s health and lifestyle conditions enables the patient to input their medical conditions to the system [ 53 - 59 ]. An analysis of personal health records management platforms based on users’ perception shows that a simple easy-to-use system is required for patient engagement and satisfaction [ 60 ].…”

Section: Resultsmentioning

confidence: 99%

Requirements of Health Data Management Systems for Biomedical Care and Research: Scoping Review

et al. 2020

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Background Over the last century, disruptive incidents in the fields of clinical and biomedical research have yielded a tremendous change in health data management systems. This is due to a number of breakthroughs in the medical field and the need for big data analytics and the Internet of Things (IoT) to be incorporated in a real-time smart health information management system. In addition, the requirements of patient care have evolved over time, allowing for more accurate prognoses and diagnoses. In this paper, we discuss the temporal evolution of health data management systems and capture the requirements that led to the development of a given system over a certain period of time. Consequently, we provide insights into those systems and give suggestions and research directions on how they can be improved for a better health care system. Objective This study aimed to show that there is a need for a secure and efficient health data management system that will allow physicians and patients to update decentralized medical records and to analyze the medical data for supporting more precise diagnoses, prognoses, and public insights. Limitations of existing health data management systems were analyzed. Methods To study the evolution and requirements of health data management systems over the years, a search was conducted to obtain research articles and information on medical lawsuits, health regulations, and acts. These materials were obtained from the Institute of Electrical and Electronics Engineers, the Association for Computing Machinery, Elsevier, MEDLINE, PubMed, Scopus, and Web of Science databases. Results Health data management systems have undergone a disruptive transformation over the years from paper to computer, web, cloud, IoT, big data analytics, and finally to blockchain. The requirements of a health data management system revealed from the evolving definitions of medical records and their management are (1) medical record data, (2) real-time data access, (3) patient participation, (4) data sharing, (5) data security, (6) patient identity privacy, and (7) public insights. This paper reviewed health data management systems based on these 7 requirements across studies conducted over the years. To our knowledge, this is the first analysis of the temporal evolution of health data management systems giving insights into the system requirements for better health care. Conclusions There is a need for a comprehensive real-time health data management system that allows physicians, patients, and external users to input their medical and lifestyle data into the system. The incorporation of big data analytics will aid in better prognosis or diagnosis of the diseases and the prediction of diseases. The prediction results will help in the development of an effective prevention plan.

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Section: Resultsmentioning

confidence: 99%

Requirements of Health Data Management Systems for Biomedical Care and Research: Scoping Review

et al. 2020

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“…Health monitoring systems and real time disease diagnosis have been one of the most important applications of IoT technology. The authors in [139][140][141][142][143][144][145][146][147][148][149] develop cloud based health monitoring systems for detecting various types of diseases, such as heart (stroke [140], irregular sound [142], irregular rhythm [144,146]), epileptic seizures [145], Parkinson seizure [149] and multiple disease diagnosis systems [147,148]. In [147,148], the authors formulate the problem of disease diagnosis as a classification problem and utilize medical data such as ECGs, EEG, heart rate, blood pressure, blood sugar, heart sound, blood glucose, liver health along with various machine and deep learning methods to achieve this task.…”

Section: Smart Healthmentioning

confidence: 99%

“…As with the other two systems, this system also had a cloud architecture. The authors in [143] also present a regression based health prognosis system for the industry using a CNN on machine data (Images, stress, temperature, vibration, position and electromagnetic signal measurements). The use of IoT based AI in Smart Industry has been presented in Table 10.…”

Section: Smart Industrymentioning

confidence: 99%

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.

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“…In addition to data access, the participation of patients in providing health conditions and lifestyle data to the physicians will aid in better prognosis and diagnosis. The introduction of IoT-based health data management system involving sensors and medical devices that monitor a patient's health and lifestyle conditions enables the patient to input their medical conditions to the system [53][54][55][56][57][58][59]. An analysis of personal health records management platforms based on users' perception shows that a simple easy-to-use system is required for patient engagement and satisfaction [60].…”

Section: Patient Participationmentioning

confidence: 99%

Requirements of Health Data Management Systems for Biomedical Care and Research: Scoping Review (Preprint)

Ismail¹,

Materwala²,

Karduck³

et al. 2019

Preprint

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BACKGROUND

Over the last century, disruptive incidents in the fields of clinical and biomedical research have yielded a tremendous change in health data management systems. This is due to a number of breakthroughs in the medical field and the need for big data analytics and the Internet of Things (IoT) to be incorporated in a real-time smart health information management system. In addition, the requirements of patient care have evolved over time, allowing for more accurate prognoses and diagnoses. In this paper, we discuss the temporal evolution of health data management systems and capture the requirements that led to the development of a given system over a certain period of time. Consequently, we provide insights into those systems and give suggestions and research directions on how they can be improved for a better health care system.

OBJECTIVE

This study aimed to show that there is a need for a secure and efficient health data management system that will allow physicians and patients to update decentralized medical records and to analyze the medical data for supporting more precise diagnoses, prognoses, and public insights. Limitations of existing health data management systems were analyzed.

METHODS

To study the evolution and requirements of health data management systems over the years, a search was conducted to obtain research articles and information on medical lawsuits, health regulations, and acts. These materials were obtained from the Institute of Electrical and Electronics Engineers, the Association for Computing Machinery, Elsevier, MEDLINE, PubMed, Scopus, and Web of Science databases.

RESULTS

Health data management systems have undergone a disruptive transformation over the years from paper to computer, web, cloud, IoT, big data analytics, and finally to blockchain. The requirements of a health data management system revealed from the evolving definitions of medical records and their management are (1) medical record data, (2) real-time data access, (3) patient participation, (4) data sharing, (5) data security, (6) patient identity privacy, and (7) public insights. This paper reviewed health data management systems based on these 7 requirements across studies conducted over the years. To our knowledge, this is the first analysis of the temporal evolution of health data management systems giving insights into the system requirements for better health care.

CONCLUSIONS

There is a need for a comprehensive real-time health data management system that allows physicians, patients, and external users to input their medical and lifestyle data into the system. The incorporation of big data analytics will aid in better prognosis or diagnosis of the diseases and the prediction of diseases. The prediction results will help in the development of an effective prevention plan.

show abstract

An Integrative Framework for Online Prognostic and Health Management Using Internet of Things and Convolutional Neural Network

Cited by 19 publications

References 42 publications

Requirements of Health Data Management Systems for Biomedical Care and Research: Scoping Review

Requirements of Health Data Management Systems for Biomedical Care and Research: Scoping Review

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

Requirements of Health Data Management Systems for Biomedical Care and Research: Scoping Review (Preprint)

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