Supporting Diabetic Patients with a Remote Patient Monitoring Systems

Zulj, Sara; Šeketa, Goran; Džaja, Dominik; Sklebar, F.; Drobnjak, Siniša; Celić, Luka; Magjarević, Ratko

doi:10.1007/978-981-10-4086-3_145

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…The symbol () indicates that the research paper uses the checked technology and the opposite is indicated by the symbol (). [15] Chronic diseases [2] Cardiovascular [26] Heart diseases [152] Ubiquitous monitoring system [28] Pain assessment [29] Heart diseases [40] Knees rehabilitation [153] Vital signs gathering and processing [46] Chronic diseases [47] Hypertension [57] Tracking daily activities [61] EXP carried on healthy volunteers [92] Context aware monitoring [77] Diabetes and Diet monitoring [96] Heart diseases [97] Diabetes [90] Diabetes [93] Mental disorder [154] Chronic diseases [155] Monitor patients with depression [131] Cardiovascular diseases [156] Hypertension, hypotension [157] Diabetes [158] Heart diseases [159] Knee arthroplasty [160] Elderly [161] Diabetes [162] Parkinson's disease [106] Fall detection [117] Diabetes [116] Alzheimer's Currently, many health monitoring projects and applications have been initiated that use different architectures. Health monitoring systems are heterogeneous and have been developed for various diseases and disabilities.…”

Section: Study Resultsmentioning

confidence: 99%

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

et al. 2021

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Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMs.

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Section: Study Resultsmentioning

confidence: 99%

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

et al. 2021

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“…We proposed a deep learning model based on X-rays of the chest and transfer learning, to classify the patient as either infected or normal, and we achieved promising results. Monitor patients with depression [36] Cardiovascular diseases × [108] Heart failure × [109] Parkinson × × [110] Congestive heart failure × [111] Hypertension, hypotension × [112] Diabetes × × × [113] Heart diseases × × × [114] Heart diseases × × [115] Elderly × [116] Diabetes × × × [117] Parkinson's disease × × [118] Chronic diseases × × [58] Diabetes × × ×…”

Section: Discussionmentioning

confidence: 99%

End-To-End Deep Learning Framework for Coronavirus (COVID-19) Detection and Monitoring

et al. 2020

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Coronavirus (COVID-19) is a new virus of viral pneumonia. It can outbreak in the world through person-to-person transmission. Although several medical companies provide cooperative monitoring healthcare systems, these solutions lack offering of the end-to-end management of the disease. The main objective of the proposed framework is to bridge the current gap between current technologies and healthcare systems. The wireless body area network, cloud computing, fog computing, and clinical decision support system are integrated to provide a comprehensive and complete model for disease detection and monitoring. By monitoring a person with COVID-19 in real time, physicians can guide patients with the right decisions. The proposed framework has three main layers (i.e., a patient layer, cloud layer, and hospital layer). In the patient layer, the patient is tracked through a set of wearable sensors and a mobile app. In the cloud layer, a fog network architecture is proposed to solve the issues of storage and data transmission. In the hospital layer, we propose a convolutional neural network-based deep learning model for COVID-19 detection based on patient’s X-ray scan images and transfer learning. The proposed model achieved promising results compared to the state-of-the art (i.e., accuracy of 97.95% and specificity of 98.85%). Our framework is a useful application, through which we expect significant effects on COVID-19 proliferation and considerable lowering in healthcare expenses.

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“…In a context of diabetic patients monitoring [27], setting up tools for accident detection is not possible without considering the necessary role that the physician must have. The aim is to design a system for assisted monitoring and diagnosis that will provide specialists with the necessary information for identifying the diabetes type of patients.…”

Section: Classification By Inductive Learningmentioning

confidence: 99%

Fuzzy Boolean Reasoning for Diagnosis of Diabetes

Benamina¹,

Atmani²,

Benbelkacem³

2018

Computer Science &Amp; Information Technology (CS &Amp; IT)

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The classification by inductive learning finds its originality in the fact that humans often use it to resolve and to handle very complex situations in their daily lives. However, the induction in humans is often approximate rather than exact. Indeed, the human brain is able to handle imprecise, vague, uncertain and incomplete information. Also, the human brain is able to learn and to operate in a context where uncertainty management is indispensable. In this paper, we propose a Boolean model of fuzzy reasoning for indexing the monitoring sub-plans, based on characteristics of the classification by inductive learning. Several competing motivations have led us to define a Boolean model for CBR knowledge base systems. Indeed, we have not only desired experiment with a new approach to indexing of cases by fuzzy decision tree, but we also wanted to improve modelling of the vague and uncertain of the natural language concepts, optimize response time and the storage complexity.

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Supporting Diabetic Patients with a Remote Patient Monitoring Systems

Cited by 5 publications

References 5 publications

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

End-To-End Deep Learning Framework for Coronavirus (COVID-19) Detection and Monitoring

Fuzzy Boolean Reasoning for Diagnosis of Diabetes

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