Digital Tools and AI in Respiratory Health

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The general aim of this research area in the UGH is to analyse complex health data using innovative artificial intelligence (AI) methods for biomarker discovery and disease prediction on both individual and population level. While biomarker discovery using AI tools and the development of digital tools are paving the way for a more individualized health care approach, the analysis and integration of large population-based datasets will enable the detection of major health threats and the prediction of long-term health on population level.

Biomarkers and digital tools

In collaborative work with the Computational Health Center, we are using cutting-edge methods in machine learning (ML) and AI for understanding and integrating multimodal medical datasets. This includes data from various sources such as randomised clinical trials, electronic health records, genomic biobanks, and more. Our research extends beyond clinical and genomic information, encompassing the exploration of additional variables to enhance our understanding of patients' disease states and progression. In multiple collaborations with large clinical research consortia, industry partners, and research institutions across different countries we are aiming for predicting patient outcomes, discover new biomarkers, forecast side effects, and enable personalised treatments tailored to individual patient needs.

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Pandemic preparedness and population-based disease prevention

With our research we further want to contribute to a broader disease prevention strategy focusing on the comprehensive study of health and disease within populations and in interrelations with environmental influences. The UGH is involved in building comprehensive health data repositories, encompassing disease epidemiology, environmental and multi-omics data, as well as electronic health records and imaging. We analyse and model health data to understand long-term health trends and the impact of major events such as pandemics, environmental disasters, and specific diseases on health outcomes at both individual and population levels. The outcomes of our research will be highly relevant to policy makers and health system stakeholders, serving as a basis for adapting population-level health interventions and strategies and planning healthcare delivery in diverse global settings.