EHealth monitoring testb e d with fuzzy based early warning score system

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dc.authoridCICIOGLU, MURTAZA/0000-0002-5657-7402
dc.authorwosidCICIOGLU, MURTAZA/AAL-5004-2020
dc.contributor.authorCalhan, Ali
dc.contributor.authorCicioglu, Murtaza
dc.contributor.authorCeylan, Arif
dc.date.accessioned2021-12-01T18:48:26Z
dc.date.available2021-12-01T18:48:26Z
dc.date.issued2021
dc.department[Belirlenecek]en_US
dc.description.abstractBackground and objective: EHealth monitoring systems are able to save the persons' lives and track some vital physiological signs of patients, sportsmen, and soldiers for some purposes. Instant data tracking enables appropriate clinical interventions. The early warning score concept defines that specific vital human body signs that are considered together and gives the persons' health score. The patient's vital signs are periodically recorded with the Early Warning Score (EWS) system and the illness severity score of the patient is decided manually. The aim of the study is to monitor a person's health data continuously and calculate the EWS score thanks to the fuzzy logic. Therefore, the simulation as a testbed is constructed for real-time applications with ISO/IEEE 11073 Health informatics -Medical/health device communication standard. Methods: In our paper, a fuzzy-based early warning score system in the EHealth monitoring testbed is proposed. Real-time data are obtained from Riverbed Modeler simulation software with socket programming and stored in the InfluxDB using Node-Red and monitored on the remote desktop with Grafana. Results: Heart rate, body temperature, systolic blood pressure, respiratory rate, and SPO2 are taken into consideration in the fuzzy-based evaluation system for EWS. The data produced in the Riverbed has been provided in a realistic manner because the real human vital sign values are considered during generating vital signs. Conclusions: Using real-time Riverbed Modeler health data with fuzzy-based EWS, a more realistic testbed platform is constructed in this study. (c) 2021 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.cmpb.2021.106008
dc.identifier.issn0169-2607
dc.identifier.issn1872-7565
dc.identifier.pmid33640651en_US
dc.identifier.scopus2-s2.0-85101355321en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.cmpb.2021.106008
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10533
dc.identifier.volume202en_US
dc.identifier.wosWOS:000639096300008en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakPubMeden_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Ireland Ltden_US
dc.relation.ispartofComputer Methods And Programs In Biomedicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEHealth monitoringen_US
dc.subjectInternet of Thingsen_US
dc.subjectEarly warning scoreen_US
dc.subjectFuzzy logicen_US
dc.titleEHealth monitoring testb e d with fuzzy based early warning score systemen_US
dc.typeArticleen_US

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