Publication: Predicting the Urgency Demand of COPD Patients From Environmental Sensors Within Smart Cities With High-Environmental Sensitivity
dc.contributor.author | Medina Quero, Javier | |
dc.contributor.author | Lopez Medina, Miguel Angel | |
dc.contributor.author | Salguero Hidalgo, Alberto | |
dc.contributor.author | Espinilla, 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.funder | REMIND Project Marie Sklodowska-Curie EU | |
dc.contributor.funder | Council of Health for the Andalusian Health Service, Spain | |
dc.contributor.funder | Spanish Government | |
dc.date.accessioned | 2023-02-12T02:21:28Z | |
dc.date.available | 2023-02-12T02:21:28Z | |
dc.date.issued | 2018-01-01 | |
dc.description.abstract | Predicting 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.doi | 10.1109/ACCESS.2018.2828652 | |
dc.identifier.issn | 2169-3536 | |
dc.identifier.unpaywallURL | https://doi.org/10.1109/access.2018.2828652 | |
dc.identifier.uri | http://hdl.handle.net/10668/18966 | |
dc.identifier.wosID | 433464400001 | |
dc.journal.title | Ieee access | |
dc.journal.titleabbreviation | Ieee access | |
dc.language.iso | en | |
dc.organization | Servicio Andaluz de Salud-SAS | |
dc.page.number | 25081-25089 | |
dc.publisher | Ieee-inst electrical electronics engineers inc | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
dc.rights.accessRights | open access | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | Predicting urgency demand | |
dc.subject | long short-term memory | |
dc.subject | temporal aggregation | |
dc.subject | fuzzy linguistic approach | |
dc.subject | Obstructive pulmonary-disease | |
dc.subject | Linguistic term sets | |
dc.subject | Hospital admissions | |
dc.subject | City | |
dc.subject | Pollen | |
dc.title | Predicting the Urgency Demand of COPD Patients From Environmental Sensors Within Smart Cities With High-Environmental Sensitivity | |
dc.type | research article | |
dc.type.hasVersion | VoR | |
dc.volume.number | 6 | |
dc.wostype | Article | |
dspace.entity.type | Publication |