Explainable Gated Recurrent Unit to explore the effect of co-exposure to multiple air pollutants and meteorological conditions on mental health outcomes

Yi, Qing; Chen, Zhenhua

doi:10.1016/j.envint.2022.107689

Cited by 5 publications

(2 citation statements)

References 63 publications

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“…While these models are state-of-the-art in computer science, their application in heat-health studies is still emerging, as demonstrated in a review of the literature on deep learning and ensemble tree-based machine learning models. [55][56][57][58][59][60][61][62][63][64][65][66] However, recognising that simpler statistical models may be effective, we plan to build on the work by Boudreault et al to compare the performance of deep learning models with tree-based approaches and nonlinear statistical models in our analysis. 57 Throughout this process, we will assess the significance of predictors for different populations within the two cities.…”

Section: Managing Biasmentioning

confidence: 99%

Leveraging data science and machine learning for urban climate adaptation in two major African cities: a HE²AT Center study protocol

Jack,

Parker,

Kouakou

et al. 2024

BMJ Open

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IntroductionAfrican cities, particularly Abidjan and Johannesburg, face challenges of rapid urban growth, informality and strained health services, compounded by increasing temperatures due to climate change. This study aims to understand the complexities of heat-related health impacts in these cities. The objectives are: (1) mapping intraurban heat risk and exposure using health, socioeconomic, climate and satellite imagery data; (2) creating a stratified heat–health forecast model to predict adverse health outcomes; and (3) establishing an early warning system for timely heatwave alerts. The ultimate goal is to foster climate-resilient African cities, protecting disproportionately affected populations from heat hazards.Methods and analysisThe research will acquire health-related datasets from eligible adult clinical trials or cohort studies conducted in Johannesburg and Abidjan between 2000 and 2022. Additional data will be collected, including socioeconomic, climate datasets and satellite imagery. These resources will aid in mapping heat hazards and quantifying heat–health exposure, the extent of elevated risk and morbidity. Outcomes will be determined using advanced data analysis methods, including statistical evaluation, machine learning and deep learning techniques.Ethics and disseminationThe study has been approved by the Wits Human Research Ethics Committee (reference no: 220606). Data management will follow approved procedures. The results will be disseminated through workshops, community forums, conferences and publications. Data deposition and curation plans will be established in line with ethical and safety considerations.

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Section: Managing Biasmentioning

confidence: 99%

Leveraging data science and machine learning for urban climate adaptation in two major African cities: a HE²AT Center study protocol

Jack,

Parker,

Kouakou

et al. 2024

BMJ Open

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“…However, there is currently no widely used guideline for identifying patients at risk, and predicting the likelihood of an asthma attack is largely a qualitative process. Many studies on the subject have used univariate regression modeling to identify asthma attack risk factors, but this method does not determine the optimal combination of factors for each patient's predictive performance [40][41][42].…”

Section: Plos Onementioning

confidence: 99%

Use of feature importance statistics to accurately predict asthma attacks using machine learning: A cross-sectional cohort study of the US population

Huang,

Huang

2023

PLoS ONE

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Background Asthma attacks are a major cause of morbidity and mortality in vulnerable populations, and identification of associations with asthma attacks is necessary to improve public awareness and the timely delivery of medical interventions. Objective The study aimed to identify feature importance of factors associated with asthma in a representative population of US adults. Methods A cross-sectional analysis was conducted using a modern, nationally representative cohort, the National Health and Nutrition Examination Surveys (NHANES 2017–2020). All adult patients greater than 18 years of age (total of 7,922 individuals) with information on asthma attacks were included in the study. Univariable regression was used to identify significant nutritional covariates to be included in a machine learning model and feature importance was reported. The acquisition and analysis of the data were authorized by the National Center for Health Statistics Ethics Review Board. Results 7,922 patients met the inclusion criteria in this study. The machine learning model had 55 out of a total of 680 features that were found to be significant on univariate analysis (P<0.0001 used). In the XGBoost model the model had an Area Under the Receiver Operator Characteristic Curve (AUROC) = 0.737, Sensitivity = 0.960, NPV = 0.967. The top five highest ranked features by gain, a measure of the percentage contribution of the covariate to the overall model prediction, were Octanoic Acid intake as a Saturated Fatty Acid (SFA) (gm) (Gain = 8.8%), Eosinophil percent (Gain = 7.9%), BMXHIP–Hip Circumference (cm) (Gain = 7.2%), BMXHT–standing height (cm) (Gain = 6.2%) and HS C-Reactive Protein (mg/L) (Gain 6.1%). Conclusion Machine Learning models can additionally offer feature importance and additional statistics to help identify associations with asthma attacks.

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A Review of the Interactive Effects of Climate and Air Pollution on Human Health in China

Yan,

2024

Curr Envir Health Rpt

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Explainable Gated Recurrent Unit to explore the effect of co-exposure to multiple air pollutants and meteorological conditions on mental health outcomes

Cited by 5 publications

References 63 publications

Leveraging data science and machine learning for urban climate adaptation in two major African cities: a HE²AT Center study protocol

Leveraging data science and machine learning for urban climate adaptation in two major African cities: a HE²AT Center study protocol

Use of feature importance statistics to accurately predict asthma attacks using machine learning: A cross-sectional cohort study of the US population

A Review of the Interactive Effects of Climate and Air Pollution on Human Health in China

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Explainable Gated Recurrent Unit to explore the effect of co-exposure to multiple air pollutants and meteorological conditions on mental health outcomes

Cited by 5 publications

References 63 publications

Leveraging data science and machine learning for urban climate adaptation in two major African cities: a HE2AT Center study protocol

Leveraging data science and machine learning for urban climate adaptation in two major African cities: a HE2AT Center study protocol

Use of feature importance statistics to accurately predict asthma attacks using machine learning: A cross-sectional cohort study of the US population

A Review of the Interactive Effects of Climate and Air Pollution on Human Health in China

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Product

Resources

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Leveraging data science and machine learning for urban climate adaptation in two major African cities: a HE²AT Center study protocol

Leveraging data science and machine learning for urban climate adaptation in two major African cities: a HE²AT Center study protocol