Logic-Centered Architecture for Ubiquitous Health Monitoring

Lewandowski, Jerzy; Arochena, Hisbel; Naguib, R.N.G.; Chao, Kuo‐Ming; García-Pérez, Alexeis

doi:10.1109/jbhi.2014.2312352

Cited by 18 publications

(10 citation statements)

References 18 publications

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“…used for automated diagnosis of different major diseases, such as breast, pancreatic and lung cancer, heart attacks, diabetes" Logic-Centered Architecture for Ubiquitous Health Monitoring [34] 2014…”

Section: -5mentioning

confidence: 99%

Trends in telemedicine utilizing artificial intelligence

Pacis

Subido

Bugtai

2018

AIP Conference Proceedings

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Abstract.With the growth and popularity of the utilization of artificial intelligence (AI) in several fields and industries, studies in the field of medicine have begun to implement its capabilities in handling and analyzing data to telemedicine. With the challenges in the implementation of telemedicine, there has been a need to expand its capabilities and improve procedures to be specialized to solve specific problems. The versatility and flexibility of both AI and telemedicine gave the endless possibilities for development and these can be seen in the literature reviewed in this paper. The trends in the development of the utilization of this technology can be classified in to four: patient monitoring, healthcare information technology, intelligent assistance diagnosis, and information analysis collaboration. Each trend will be discussed and presented with examples of recent literature and the problems they aim to address. Related references will also be tabulated and categorized to see the future and potential of this current trend in telemedicine.

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Section: -5mentioning

confidence: 99%

Trends in telemedicine utilizing artificial intelligence

Pacis

Subido

Bugtai

2018

AIP Conference Proceedings

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“…The proposed high-level system architecture, presented in Figure 3, is based on a three-tier logic centered architecture for ubiquitous health monitoring discussed in [22]. It consists of three elements: the wireless pH and temperature sensor node, the smartphone device responsible for data aggregation and processing, and remote web services for data analysis.…”

Section: Intra-vaginal Ph Monitoring Systemmentioning

confidence: 99%

Design Concepts and Operation Principles for Smart Intra-Vaginal Health Monitoring Devices

Lewandowski

Chao

Niżański

2016

2016 IEEE 13th International Conference on E-Business Engineering (ICEBE)

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Abstract-The proposed work is the conception of a novel intra-body sensor device capable of measuring and gathering long-term intra-vaginal pH and temperature. At its early stage this project focuses on the analysis of different design concepts and operation principles of such intra vaginal sensor device. Traditional methods of intra-vaginal pH sampling, which rely on analysis of vaginal secretions or discharge from lateral vaginal walls, are difficult in long-term and often fraught with the potential for errors. Therefore this paper focuses on the analysis of such sensor's shape, form, size and fabric in order to enable precise, continuous acquisition of females' and animals' intra-vaginal pH and temperature at the right place inside the vagina. The aim is to set such operation principles, which will enable convenient collection and analysis of continuous vaginal pH and temperature samples for the purpose of further study. The objective is to achieve at least as accurate results as with the discrete analysis of clinicallyobtained intra-vaginal specimens, if not better. The data obtained with such sensors could enable a development of insemination prediction and gynaecological disorders detection models, which deployed to, for example smartphone devices, will help to attain high conception rate for animals and eventually improve the control of the vaginal health for women.

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“…To solve this issue, an alternative is to use computer to analyze big data so that the accuracy of diagnostic result can be enhanced. With development of artificial intelligence, this strategy has been applied in diverse areas of healthcare [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Bone Metabolic Biomarkers-Based Diagnosis of Osteoporosis Caused by Diabetes Mellitus using Support Vector Machine

Wang¹,

Zhang

et al. 2020

Preprint

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Background: Diabetes has significant effects on bone metabolism. Both type 1 and type 2 diabetes can cause osteoporotic fracture. However, it remains challenging to diagnose osteoporosis in type 2 diabetes by bone mineral density which lacks regular changes. Seen another way, osteoporosis can be ascribed to the imbalance of bone metabolism, which is closely related to diabetes as well. Method: Here, to assist clinicians in diagnosing osteoporosis in type 2 diabetes, an efficient and simple SVM model was established based on different combinations of biochemical indices, including bone turnover makers, calcium and phosphorus, etc. The classification performance was measured using several evaluations. Results: The predicting accuracy rate of final model is above 88%, with feature combination of Sex, Age, BMI, TP1NP and OSTEOC. Conclusion: Experimental results show that the model has come to an anticipant result for early detection and daily monitoring on type 2 diabetic osteoporosis.

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Logic-Centered Architecture for Ubiquitous Health Monitoring

Cited by 18 publications

References 18 publications

Trends in telemedicine utilizing artificial intelligence

Trends in telemedicine utilizing artificial intelligence

Design Concepts and Operation Principles for Smart Intra-Vaginal Health Monitoring Devices

Bone Metabolic Biomarkers-Based Diagnosis of Osteoporosis Caused by Diabetes Mellitus using Support Vector Machine

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