Short-term effects of ambient air pollution and cardiovascular events in Shiraz, Iran, 2009 to 2015

Soleimani, Zahra; Boloorani, Ali Darvishi; Khalifeh, Reza; Griffin, Dale W.; Mesdaghinia, Alireza

doi:10.1007/s11356-018-3952-4

Cited by 39 publications

(29 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is the first study to construct and compare various machine learning models in terms of predicting the peak events of CVDs admissions using meteorological data, air quality data and hospital admissions data. The lag patterns of meteorological conditions and air pollutants have been well-documented in epidemiological studies [8,12,16], and suggest that the lag effects of environmental exposure have regional differences. However, to date, very few machine-learning based studies have analyzed the lag effect of environmental exposure when predicting the peak demand for healthcare services.…”

Section: Discussionmentioning

confidence: 91%

“…Environmental exposure, such as ambient air pollution and extreme temperatures, is an important but underappreciated risk factor contributing to the development and severity of CVDs [4]. Accumulating evidence from epidemiological studies has linked environmental exposure to increased risk of CVDs morbidity [5][6][7][8][9][10][11][12]. However, evidence of the effect of a complex mixture of environmental exposure on…”

Section: Discussionmentioning

confidence: 99%

“…Accumulating epidemiological studies have suggested that the effect of meteorological conditions and air pollutants on CVDs admissions is delayed, and the lag effect is related to the regional environment [8,12,37]. Hence, we employed an over-dispersed GAM, which allowed the quasi-Poisson distribution to analyze the lag effects of daily meteorological conditions and air pollutants on CVDs admissions, and chose the lag day based on the minimum Generalized Cross-Validation (GCV) values which 7 measure models fit [5,34].…”

Section: Feature Extractionmentioning

confidence: 99%

“…Cardiovascular diseases (CVDs) are the leading cause of death worldwide; about 17.9 million deaths were attributable to CVDs in 2016, representing approximately 31% of all global deaths in that year [1]. Even though behavioral factors, including physical inactivity, smoking, unhealthy diets and obesity, are well-known risk factors for CVDs, a large body of studies have indicated that environmental exposure [2][3][4], such as ambient air pollution [5][6][7][8][9] and temperature variability [10][11][12], also makes a significant contribution to CVDs, resulting in increased risk of morbidity. For example, using conditional logistic regression models, Liu et al [13] conducted a multi-city study in 26 Chinese cities, and the results showed that elevated concentrations of sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), carbon monoxide (CO), and ozone (O 3 ) were associated with increased risk of hospitalization for heart failure.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Machine Learning Approaches to Predict Peak Demand Days of Cardiovascular Admissions Considering Environmental Exposure

Qiu

Luo

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Accumulating evidence has linked environmental exposure, such as ambient air pollution and meteorological factors, to the development and severity of cardiovascular diseases (CVDs), resulting in increased healthcare demand. Effective prediction of demand for healthcare services, particularly those associated with peak events of CVDs, can be useful in optimizing the allocation of medical resources. However, few studies have attempted to adopt machine learning approaches with excellent predictive abilities to forecast the healthcare demand for CVDs. This study aims to develop and compare several machine learning models in predicting the peak demand days of CVDs admissions using the hospital admissions data, air quality data and meteorological data in Chengdu, China from 2015 to 2017.Methods: Six machine learning algorithms, including logistic regression (LR), support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) were applied to build the predictive models with a unique feature set. The area under a receiver operating characteristic curve (AUC), logarithmic loss function, accuracy, sensitivity, specificity, precision, and F1 score were used to evaluate the predictive performances of the six models.Results: The LightGBM model exhibited the highest AUC (0.940, 95% CI: 0.900-0.980), which was significantly higher than that of LR (0.842, 95% CI: 0.783-0.901), SVM (0.834, 95% CI: 0.774-0.894) and ANN (0.890, 95% CI: 0.836-0.944), but did not differ significantly from that of RF (0.926, 95% CI: 0.879-0.974) and XGBoost (0.930, 95% CI: 0.878-0.982). In addition, the LightGBM has the optimal logarithmic loss function (0.218), accuracy (91.3%), specificity (94.1%), precision (0.695), and F1 score (0.725). Feature importance identification indicated that the contribution rate of meteorological conditions and air pollutants for the prediction was 32% and 43%, respectively.Conclusion: This study suggests that ensemble learning models, especially the LightGBM model, can be used to effectively predict the peak events of CVDs admissions, and therefore could be a very useful decision-making tool for medical resource management.

show abstract

Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Approaches to Predict Peak Demand Days of Cardiovascular Admissions Considering Environmental Exposure

Qiu

Luo

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure

Qiu

Luo

et al. 2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background: Accumulating evidence has linked environmental exposure, such as ambient air pollution and meteorological factors, to the development and severity of cardiovascular diseases (CVDs), resulting in increased healthcare demand. Effective prediction of demand for healthcare services, particularly those associated with peak events of CVDs, can be useful in optimizing the allocation of medical resources. However, few studies have attempted to adopt machine learning approaches with excellent predictive abilities to forecast the healthcare demand for CVDs. This study aims to develop and compare several machine learning models in predicting the peak demand days of CVDs admissions using the hospital admissions data, air quality data and meteorological data in Chengdu, China from 2015 to 2017. Methods: Six machine learning algorithms, including logistic regression (LR), support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) were applied to build the predictive models with a unique feature set. The area under a receiver operating characteristic curve (AUC), logarithmic loss function, accuracy, sensitivity, specificity, precision, and F1 score were used to evaluate the predictive performances of the six models. Results: The LightGBM model exhibited the highest AUC (0.940, 95% CI: 0.900-0.980), which was significantly higher than that of LR (0.842, 95% CI: 0.783-0.901), SVM (0.834, 95% CI: 0.774-0.894) and ANN (0.890, 95% CI: 0.836-0.944), but did not differ significantly from that of RF (0.926, 95% CI: 0.879-0.974) and XGBoost (0.930, 95% CI: 0.878-0.982). In addition, the LightGBM has the optimal logarithmic loss function (0.218), accuracy (91.3%), specificity (94.1%), precision (0.695), and F1 score (0.725). Feature importance identification indicated that the contribution rate of meteorological conditions and air pollutants for the prediction was 32 and 43%, respectively.

show abstract

Ambient temperature and cardiovascular disease-related emergency room visits in Lanzhou, China from 2013 to 2019: A time-series study

Ye,

Ma,

Dong

et al. 2024

Int J Biometeorol

View full text Add to dashboard Cite

Short-term effects of ambient air pollution and cardiovascular events in Shiraz, Iran, 2009 to 2015

Cited by 39 publications

References 27 publications

Machine Learning Approaches to Predict Peak Demand Days of Cardiovascular Admissions Considering Environmental Exposure

Machine Learning Approaches to Predict Peak Demand Days of Cardiovascular Admissions Considering Environmental Exposure

Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure

Ambient temperature and cardiovascular disease-related emergency room visits in Lanzhou, China from 2013 to 2019: A time-series study

Contact Info

Product

Resources

About