ailslab: Artificial Intelligence in the Life Sciences

Das ailslab konzentriert sich auf innovative Methoden aus dem weiteren Feld der künstlichen Intelligenz für wegweisende digitale Gesundheitsanwendungen. Roland Eils, Leiter der Forschungsgruppe, hat ein kleines, aber höchst innovatives Team von Studierenden und Wissenschaftler*innen zusammengestellt, das sich mit verschiedenen Aspekten der digitalen Gesundheit befasst. Im Mittelpunkt dieses Labors steht die Integration riesiger und tiefgreifender Datensätze sowohl aus dem Gesundheitswesen als auch aus der Forschung zur Bewältigung dringender Probleme im Bereich Gesundheit und Krankheit. Unser Ziel ist es, gleichzeitig unser Verständnis der Krankheitsmechanismen zu vertiefen und die Diagnose und Behandlung von Herz-Kreislauf- und Krebspatienten zu verbessern.
Research topics
Cardiovascular risk modelling
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 the use of novel treatments, support personalised monitoring windows and increase the efficiency of clinical trial designs by allowing for the selection of individuals at high risk of recurrent events. Here, we aim to improve and personalise prevention and treatment of established 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. 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.
Omni-genetic phenotype models
Open medical data
Publications
*these authors contributed equally
§corresponding author