A Machine Learning Based Analysis Between Climate Change and Human Health: A Correlational Study

Fahim, Md.; Uddin, Md. Ekhtiar; Ahmed, Rizve; Islam, Md. Rashedul; Ahmed, Nadeem

doi:10.1109/icca56443.2022.10039484

Cited by 3 publications

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Interdisciplinary methods for researching climate-mental health links through the Methane Early Warning Network (ME-NET): improving ‘visibility’ and integrating complex multi-datasets

Moore,

Atanbori,

Amankwaa

et al. 2024

Res. dir. Depress.

View full text Add to dashboard Cite

Climatic and atmospheric conditions impact mental health, including increased incidents of depression associated with air pollution. A growing body of research considers time-bound ‘snap-shots’ of climatic drivers and mental health outcomes. Less is known about the likely effects of future climate change on mental health. Research is often inhibited by data scarcity, the challenge of synthesising data across multiple geospatial and temporal scales, and the under-representation of hard-to-reach groups. Thus, research methods are needed to integrate and analyse complex environmental and mental health multi-datasets while improving the visibility of under-represented groups. In this methods paper we present a novel approach for investigating the impact of climate change on mental health and addressing some challenges with, a) invisibility of vulnerable groups, and b) integrating complex environmental and mental health multi-datasets. The research aim is to pilot a web-based and smartphone application (Methane Early Warning Network (ME-NET)) for investigating the role of methane as a precursor of on-ground ozone, and the impact of ozone on mental health outcomes to improve civic knowledge and health-protection behaviour in the United Kingdom and Ghana. The methods include exploring the feasibility of using machine learning to develop an ozone early warning system and application co-design.

show abstract

Interdisciplinary methods for researching climate-mental health links through the Methane Early Warning Network (ME-NET): improving ‘visibility’ and integrating complex multi-datasets

Moore,

Atanbori,

Amankwaa

et al. 2024

Res. dir. Depress.

View full text Add to dashboard Cite

show abstract

A Novel Predictive Analysis to Identify the Weather Impacts for Congenital Heart Disease Using Reinforcement Learning

Mohammed,

Ramakrishnan,

Mohammed

et al. 2023

2023 International Conference on Network, Multimedia and Information Technology (NMITCON)

View full text Add to dashboard Cite

AI Innovations in Global Health in the Context of Climate Change

Papadopoulou,

Apostolaki,

Lytras

et al. 2024

Advances in Healthcare Information Systems and Administration

View full text Add to dashboard Cite

This chapter explores the intersection of human health, climate change, and artificial intelligence (AI), emphasizing AI's role in safeguarding human health amidst climate challenges. It explores AI's potential as a solution and its contribution to climate change which poses significant health risks, including shifts in disease patterns and increased threats from infectious diseases. These risks manifest through extreme weather events and disruptions to essential services like water and food supplies. By leveraging AI technologies, tailored responses can enhance human health resilience, including early warning systems and optimized resource allocation. The review underscores AI's promise in transforming human health resilience and readiness. However, it stresses the need to address ethical and equity concerns for responsible deployment at the individual, public or global health level. Collaboration across sectors is crucial for maximizing AI's potential in safeguarding human health.

show abstract

A Machine Learning Based Analysis Between Climate Change and Human Health: A Correlational Study

Cited by 3 publications

References 9 publications

Interdisciplinary methods for researching climate-mental health links through the Methane Early Warning Network (ME-NET): improving ‘visibility’ and integrating complex multi-datasets

Interdisciplinary methods for researching climate-mental health links through the Methane Early Warning Network (ME-NET): improving ‘visibility’ and integrating complex multi-datasets

A Novel Predictive Analysis to Identify the Weather Impacts for Congenital Heart Disease Using Reinforcement Learning

AI Innovations in Global Health in the Context of Climate Change

Contact Info

Product

Resources

About