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Ahmidi Lab:
AI for Patient Diagnosis and Treatment

The overarching theme of our research is to develop causally-informed predictive models for healthcare applications.

The overarching theme of our research is to develop causally-informed predictive models for healthcare applications.

About our lab

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.

 

Publications

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M. Mustermann, P. Mustermann, C. Mustermann

M. Mustermann, P. Mustermann, C. Mustermann

Contact

Dr. Narges Ahmidi

Research Group Leader

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