RT Journal Article
T1 Predicting the Urgency Demand of COPD Patients From Environmental Sensors Within Smart Cities With High-Environmental Sensitivity
A1 Medina Quero, Javier
A1 Lopez Medina, Miguel Angel
A1 Salguero Hidalgo, Alberto
A1 Espinilla, Macarena
K1 Predicting urgency demand
K1 long short-term memory
K1 temporal aggregation
K1 fuzzy linguistic approach
K1 Obstructive pulmonary-disease
K1 Linguistic term sets
K1 Hospital admissions
K1 City
K1 Pollen
AB 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.
PB Ieee-inst electrical electronics engineers inc
SN 2169-3536
YR 2018
FD 2018-01-01
LK http://hdl.handle.net/10668/18966
UL http://hdl.handle.net/10668/18966
LA en
DS RISalud
RD Feb 16, 2025