Publication:
Predicting the Urgency Demand of COPD Patients From Environmental Sensors Within Smart Cities With High-Environmental Sensitivity

dc.contributor.authorMedina Quero, Javier
dc.contributor.authorLopez Medina, Miguel Angel
dc.contributor.authorSalguero Hidalgo, Alberto
dc.contributor.authorEspinilla, Macarena
dc.contributor.authoraffiliation[Medina Quero, Javier] Univ Jaen, Dept Comp Sci, Campus Las Lagunillas, Jaen 23071, Spain
dc.contributor.authoraffiliation[Espinilla, Macarena] Univ Jaen, Dept Comp Sci, Campus Las Lagunillas, Jaen 23071, Spain
dc.contributor.authoraffiliation[Lopez Medina, Miguel Angel] Andalusian Hlth Serv, Council Hlth, Seville 41071, Spain
dc.contributor.authoraffiliation[Salguero Hidalgo, Alberto] Univ Cadiz, Dept Comp Sci, Cadiz 11001, Spain
dc.contributor.funderREMIND Project Marie Sklodowska-Curie EU
dc.contributor.funderCouncil of Health for the Andalusian Health Service, Spain
dc.contributor.funderSpanish Government
dc.date.accessioned2023-02-12T02:21:28Z
dc.date.available2023-02-12T02:21:28Z
dc.date.issued2018-01-01
dc.description.abstractPredicting the urgency demand of patients at health centers in smart cities supposes a challenge for adapting emergency service in advance. In this paper, we propose a methodology to predict the number of cases of chronic obstructive pulmonary disease (COPD) from environmental sensors located in the city of Jaen (Spain). The approach presents a general methodology to predict events from environmental sensors within smart cities based on four stages: 1) summarize and expand features by means of temporal aggregations; 2) evaluate the correlation for selecting relevant features; 3) integrate straightforwardly expert knowledge under a fuzzy linguistic approach; and 4) predict the target event with the sequence-based classifier long short-term memory under a sliding window approach. The results show an encouraging performance of the methodology over the COPD patients of the city of Jaen based on a quantitative regression analysis and qualitative categorization of data.
dc.identifier.doi10.1109/ACCESS.2018.2828652
dc.identifier.issn2169-3536
dc.identifier.unpaywallURLhttps://doi.org/10.1109/access.2018.2828652
dc.identifier.urihttp://hdl.handle.net/10668/18966
dc.identifier.wosID433464400001
dc.journal.titleIeee access
dc.journal.titleabbreviationIeee access
dc.language.isoen
dc.organizationServicio Andaluz de Salud-SAS
dc.page.number25081-25089
dc.publisherIeee-inst electrical electronics engineers inc
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectPredicting urgency demand
dc.subjectlong short-term memory
dc.subjecttemporal aggregation
dc.subjectfuzzy linguistic approach
dc.subjectObstructive pulmonary-disease
dc.subjectLinguistic term sets
dc.subjectHospital admissions
dc.subjectCity
dc.subjectPollen
dc.titlePredicting the Urgency Demand of COPD Patients From Environmental Sensors Within Smart Cities With High-Environmental Sensitivity
dc.typeresearch article
dc.type.hasVersionVoR
dc.volume.number6
dc.wostypeArticle
dspace.entity.typePublication

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