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Modelo fuzzy estimando tempo de internação por doenças cardiovasculares

Overview of attention for article published in Ciência & Saúde Coletiva, August 2015
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Modelo fuzzy estimando tempo de internação por doenças cardiovasculares
Published in
Ciência & Saúde Coletiva, August 2015
DOI 10.1590/1413-81232015208.19472014
Pubmed ID

Karine Mayara Vieira Coutinho, Paloma Maria Silva Rocha Rizol, Luiz Fernando Costa Nascimento, Andréa Paula Peneluppi de Medeiros


A fuzzy linguistic model based on the Mamdani method with input variables, particulate matter, sulfur dioxide, temperature and wind obtained from CETESB with two membership functions each was built to predict the average hospitalization time due to cardiovascular diseases related to exposure to air pollutants in São José dos Campos in the State of São Paulo in 2009. The output variable is the average length of hospitalization obtained from DATASUS with six membership functions. The average time given by the model was compared to actual data using lags of 0 to 4 days. This model was built using the Matlab v. 7.5 fuzzy toolbox. Its accuracy was assessed with the ROC curve. Hospitalizations with a mean time of 7.9 days (SD = 4.9) were recorded in 1119 cases. The data provided revealed a significant correlation with the actual data according to the lags of 0 to 4 days. The pollutant that showed the greatest accuracy was sulfur dioxide. This model can be used as the basis of a specialized system to assist the city health authority in assessing the risk of hospitalizations due to air pollutants.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 15%
Professor 3 12%
Student > Bachelor 2 8%
Student > Master 2 8%
Lecturer 1 4%
Other 4 15%
Unknown 10 38%
Readers by discipline Count As %
Environmental Science 4 15%
Medicine and Dentistry 3 12%
Pharmacology, Toxicology and Pharmaceutical Science 2 8%
Computer Science 2 8%
Earth and Planetary Sciences 1 4%
Other 3 12%
Unknown 11 42%