AI for Patient Diagnosis and Treatment


The overarching theme of our research is to develop causally-informed predictive models for healthcare applications. Analyzing large-scale retrospective Electronic Health Records (EHR) data provides unique opportunities to develop technologies applicable in realistic and challenging clinical settings with a strong impact on individualized care. We develop:

  • AI models for prediction of disease development trajectories over time, for
    • short- and long-term clinical outcome prediction
    • identification of increased-risk patient categories
  • Causal models for measuring effectiveness of different treatment options, for
    • risk analysis of treatment decisions
    • assessing potential improvement in patient recovery for new therapies  

In this context, we are interested in quantifying the uncertainty of observations and decisions for both humans and AI, designing robust models in the presence of not-at-random missing data, and designing reliable validation metrics geared towards clinical expectations.

Our team, including colleagues, students, and staff, is now distributed across six sites:

We also collaborate with private healthcare companies across Europe, currently in Norway, Switzerland and Germany. 

Apply here:

AI Senior Scientist  (CLOSED)

 Fully funded PhD positions via Munich School for Data Science  (CLOSED)