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Estimating the average length of hospitalization due to pneumonia: a fuzzy approach.

Nascimento LF, Rizol PM, Peneluppi AP - Braz. J. Med. Biol. Res. (2014)

Bottom Line: The model was validated against real data, and a receiver operating characteristic (ROC) curve was constructed to evaluate model performance.The values predicted by the model were significantly correlated with real data.Sulfur dioxide and particulate matter significantly predicted the mean length of hospitalization in lags 0, 1, and 2.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Medicina, Universidade de Taubaté, Taubaté, SP, Brasil.

ABSTRACT
Exposure to air pollutants is associated with hospitalizations due to pneumonia in children. We hypothesized the length of hospitalization due to pneumonia may be dependent on air pollutant concentrations. Therefore, we built a computational model using fuzzy logic tools to predict the mean time of hospitalization due to pneumonia in children living in São José dos Campos, SP, Brazil. The model was built with four inputs related to pollutant concentrations and effective temperature, and the output was related to the mean length of hospitalization. Each input had two membership functions and the output had four membership functions, generating 16 rules. The model was validated against real data, and a receiver operating characteristic (ROC) curve was constructed to evaluate model performance. The values predicted by the model were significantly correlated with real data. Sulfur dioxide and particulate matter significantly predicted the mean length of hospitalization in lags 0, 1, and 2. This model can contribute to the care provided to children with pneumonia.

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Related in: MedlinePlus

Membership function to particulate matter (PM10), sulfur dioxide(SO2), ozone (O3), and effective temperature (ET), SãoJosé dos Campos, SP, Brazil, 2009.
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Related In: Results  -  Collection

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f01: Membership function to particulate matter (PM10), sulfur dioxide(SO2), ozone (O3), and effective temperature (ET), SãoJosé dos Campos, SP, Brazil, 2009.

Mentions: Each input variable was constructed with two membership functions (Figure 1), and the output variable was built with four membershipfunctions (Figure 2).


Estimating the average length of hospitalization due to pneumonia: a fuzzy approach.

Nascimento LF, Rizol PM, Peneluppi AP - Braz. J. Med. Biol. Res. (2014)

Membership function to particulate matter (PM10), sulfur dioxide(SO2), ozone (O3), and effective temperature (ET), SãoJosé dos Campos, SP, Brazil, 2009.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4230288&req=5

f01: Membership function to particulate matter (PM10), sulfur dioxide(SO2), ozone (O3), and effective temperature (ET), SãoJosé dos Campos, SP, Brazil, 2009.
Mentions: Each input variable was constructed with two membership functions (Figure 1), and the output variable was built with four membershipfunctions (Figure 2).

Bottom Line: The model was validated against real data, and a receiver operating characteristic (ROC) curve was constructed to evaluate model performance.The values predicted by the model were significantly correlated with real data.Sulfur dioxide and particulate matter significantly predicted the mean length of hospitalization in lags 0, 1, and 2.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Medicina, Universidade de Taubaté, Taubaté, SP, Brasil.

ABSTRACT
Exposure to air pollutants is associated with hospitalizations due to pneumonia in children. We hypothesized the length of hospitalization due to pneumonia may be dependent on air pollutant concentrations. Therefore, we built a computational model using fuzzy logic tools to predict the mean time of hospitalization due to pneumonia in children living in São José dos Campos, SP, Brazil. The model was built with four inputs related to pollutant concentrations and effective temperature, and the output was related to the mean length of hospitalization. Each input had two membership functions and the output had four membership functions, generating 16 rules. The model was validated against real data, and a receiver operating characteristic (ROC) curve was constructed to evaluate model performance. The values predicted by the model were significantly correlated with real data. Sulfur dioxide and particulate matter significantly predicted the mean length of hospitalization in lags 0, 1, and 2. This model can contribute to the care provided to children with pneumonia.

Show MeSH
Related in: MedlinePlus