Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12188/22369
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Dimitrievski, Ace | en_US |
dc.contributor.author | Koceski, Sasho | en_US |
dc.contributor.author | Koceska, Natasha | en_US |
dc.contributor.author | Zdravevski, Eftim | en_US |
dc.contributor.author | Lameski, Petre | en_US |
dc.contributor.author | Hrvoje Belani, | en_US |
dc.contributor.author | Trajkovikj, Vladimir | en_US |
dc.date.accessioned | 2022-08-17T08:49:19Z | - |
dc.date.available | 2022-08-17T08:49:19Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.12188/22369 | - |
dc.description.abstract | Sleep apnea is medical condition that affects about 4% of the population and may cause various medical complications such as fatigue, hearth problems and elevated blood pressure, diabetes type II, metabolic syndrome and others. Nowadays, there is a huge demand for technology solutions and new care models that will help in understanding patient’s needs and characteristics, facilitating treatment adherence and shared-decision making. This paper proposes a system and methodology based on fog computing paradigm to unobtrusively detect sleep apnea and to enable patients with sleep apnea and health care providers to be active participants and collaborate in chronic disease management. | en_US |
dc.relation.ispartof | Liječnički vjesnik, GLASILO HRVATSKOGA LIJEČNIČKOG ZBORA | en_US |
dc.title | Patient-Centered Care based on Fog Computing Paradigm-A Case of Sleep Apnea Detection | en_US |
dc.type | Article | en_US |
item.fulltext | With Fulltext | - |
item.grantfulltext | open | - |
crisitem.author.dept | Faculty of Computer Science and Engineering | - |
crisitem.author.dept | Faculty of Computer Science and Engineering | - |
crisitem.author.dept | Faculty of Computer Science and Engineering | - |
Appears in Collections: | Faculty of Computer Science and Engineering: Journal Articles |
Files in This Item:
File | Description | Size | Format | |
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BFHA_2020_2_compressed.pdf | 4.16 MB | Adobe PDF | View/Open |
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