期刊
IEEE ACCESS
卷 6, 期 -, 页码 25081-25089出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2828652
关键词
Predicting urgency demand; long short-term memory; temporal aggregation; fuzzy linguistic approach
资金
- REMIND Project Marie Sklodowska-Curie EU [734355]
- Council of Health for the Andalusian Health Service, Spain
- Spanish Government [PI-0203-2016, TIN2015-66524-P]
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.
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