Noise2NAKOAI
AI Methods linking Environment and Health - a large-scale cohort application
The project aims to develop data science methods in the domains of Artificial Intelligence (AI) and Machine Learning (ML) to advance currently available noise maps, to improve the quantification of noise impacts on health and to describe the complex interplay between environmental, contextual and individual socio-economic and health data.
Noise2NAKOAI
AI Methods linking Environment and Health - a large-scale cohort application
The project aims to develop data science methods in the domains of Artificial Intelligence (AI) and Machine Learning (ML) to advance currently available noise maps, to improve the quantification of noise impacts on health and to describe the complex interplay between environmental, contextual and individual socio-economic and health data.
Overview
Sophisticated spatial and spatio-temporal exposure models are urgently needed to better reflect real-life exposures and to comprehensively determine and understand the long-term impact of environmental factors on health. Furthermore, advanced statistical and data science approaches are needed to elucidate and understand the complex interplay between the environment and population health. Currently available models are hampered by the trade-off between complexity and interpretability as well as the biased nature of population-based cohort data. This project aims to solve these challenges by developing data science methods in the domains of Artificial Intelligence (AI) and Machine Learning (ML).
The project is Helmholtz AI project and is designed as close interdisciplinary collaboration between data science, earth observation and environmental epidemiology.
- To extend the 2017 traffic noise maps (currently only available for larger agglomerations and in the vicinity of major roads, railways and airports) by the application of Machine Learning (ML) methods instead of physical modelling approaches.
- To develop and to systematically test deep learning approaches to link noise maps with neighborhood information of the German national cohort (NAKO) study to identify vulnerable clusters in terms of noise and neighborhood for the risk of hypertension and prediction of these clusters across entire Germany.
- To extend this prediction model with individual information from NAKO participants to investigate the additional influence of individual risk factors for hypertension exploring and promoting Artificial Intelligence (AI)/ML and interpretable approaches.
Helmholtz Association's Initiative and Networking Fund (INF): ZT-I-PF-5-42 (funding period: 2021-2022)
Helmholtz Zentrum München - Institute of Epidemiology: Kathrin Wolf (PI), NN
German Aerospace Center (DLR): Hannes Taubenböck, Jeroen Staab: https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-5414/9543_read-18621/
Helmholtz Zentrum München - Department of Information and Communication Technology: Wolfgang zu Castell, Mahyar Valizade
Sophisticated spatial and spatio-temporal exposure models are urgently needed to better reflect real-life exposures and to comprehensively determine and understand the long-term impact of environmental factors on health. Furthermore, advanced statistical and data science approaches are needed to elucidate and understand the complex interplay between the environment and population health. Currently available models are hampered by the trade-off between complexity and interpretability as well as the biased nature of population-based cohort data. This project aims to solve these challenges by developing data science methods in the domains of Artificial Intelligence (AI) and Machine Learning (ML).
The project is Helmholtz AI project and is designed as close interdisciplinary collaboration between data science, earth observation and environmental epidemiology.
- To extend the 2017 traffic noise maps (currently only available for larger agglomerations and in the vicinity of major roads, railways and airports) by the application of Machine Learning (ML) methods instead of physical modelling approaches.
- To develop and to systematically test deep learning approaches to link noise maps with neighborhood information of the German national cohort (NAKO) study to identify vulnerable clusters in terms of noise and neighborhood for the risk of hypertension and prediction of these clusters across entire Germany.
- To extend this prediction model with individual information from NAKO participants to investigate the additional influence of individual risk factors for hypertension exploring and promoting Artificial Intelligence (AI)/ML and interpretable approaches.
Helmholtz Association's Initiative and Networking Fund (INF): ZT-I-PF-5-42 (funding period: 2021-2022)
Helmholtz Zentrum München - Institute of Epidemiology: Kathrin Wolf (PI), NN
German Aerospace Center (DLR): Hannes Taubenböck, Jeroen Staab: https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-5414/9543_read-18621/
Helmholtz Zentrum München - Department of Information and Communication Technology: Wolfgang zu Castell, Mahyar Valizade
