Eils Group, Hub for Innovations in Digital Health, Service center: Heidelberg Center for Human Bioinformatics – HD-HuB

In this project, we work towards an improved prevention and treatment of patients with established coronary heart disease (CHD). CHD is a major cause of morbidity and mortality. The risk landscape in patients with coronary disease is highly heterogenous, depending on their genetic background, clinical characteristics, cardiovascular risk factors and atherosclerotic disease status. With the availability of effective, but costly novel treatment options (such as PCSK9-inhibitors), there is great need for advanced cardiovascular risk prediction tools to stratify patients, guide the use of novel treatments and improve clinical trial design by selecting high-risk individuals. Here, we aim to improve and personalize prevention and treatment of coronary heart disease by developing data-driven, neural-network based tools for risk modelling and multi-modal data integration. We investigate representation learning techniques to identify latent factors across data modalities associated with risk and examine lifetime risk more closely with the aim of making individualized risk prediction using counterfactuals. The clinical environment at Charité in synergy with data-collection and patient management platforms allows for a prospective validation and clinical integration of our technologies. 

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