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Predicting the urgency demand of COPD patients from environmental sensors within smart cities with high-environmental sensitivity

TítuloPredicting the urgency demand of COPD patients from environmental sensors within smart cities with high-environmental sensitivity
Tipo de publicaciónRevista Internacional
Año de publicación2018
AutoresJ. Medina, M. Ángel Ló Medina, A. Salguero and M. Espinilla
RevistaIEEE Access
EditorialIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Lugar de publicaciónUSA
ISSN Number2169-3536
Resumen

Predicting the urgency demand of patients at Health Centers in smart cities supposes a challenge for adapting Emergency Service in advance. In this work, we propose a methodology to predict the number of cases of Chronic Obstructive Pulmonary Disease (COPD) from environmental sensors located in the city of Ja´en (Spain). The approach presents a general methodology to predict events from environmental sensors within smart cities based on four stages, which (i) summarize and expand features by means of temporal aggregations, (ii) evaluate the correlation for selecting relevant features, (iii) integrate straightforwardly expert knowledge under a fuzzy linguistic approach; and (iv) predict the target event with the sequencebased 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 Ja´en based on a quantitative regression analysis and qualitative categorization of data.

URLhttps://ieeexplore.ieee.org/document/8347090/
DOI10.1109/ACCESS.2018.2828652
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