Helmholtz Munich

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.

See all

2023 Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease

A. Bauer, E. Pachl, JC. Hellmuth, N. Kneidinger, M. Heydarian, M. Frankenberger, HC. Stubbe, B. Ryffel, A. Petrera, SM. Hauck, J. Behr, R. Kaiser, C. Scherer, L. Deng, D. Teupser, N. Ahmidi, M. Muenchhoff, B. Schubert, A. Hilgendorff

Proteomics reveals antiviral host response and NETosis during acute COVID-19 in high-risk patients

2022 The Annals of Thoracic Surgery

E. Sherman, D. Alejo, Z. Wood-Doughty, M. Sussman, S. Schena, CS. Ong, E. Etchill, J. DiNatale, N. Ahmidi, I. Shpitser, G. Whitman

Leveraging machine learning to predict 30-day hospital readmission after cardiac surgery

2022 Nutrients

A. Kindt, Y. Kraus, D. Rasp, KM. Foerster, N. Ahmidi, AW. Flemmer, S.Herber-Jonat, F. Heinen, H. Weigand, T. Hankemeier, B. Koletzko, J. Krumsiek, J. Babl, A. Hilgendorff

Improved macro-and micronutrient supply for favorable growth and metabolomic profile with standardized parenteral nutrition solutions for very preterm infants

2022 medRxiv

H. Loureiro, T. Kolben, A .Kiermaier, D. Rüttinger, N. Ahmidi, T Becker, and A. Bauer-Mehren

Longitudinal assessment of ROPRO as an early indicator of overall survival in oncology clinical trials: a retrospective analysis

2021 Advances in Clinical Decision Support System, Applied Sciences

E. Pachl, A. Zamanian, M. Stieler, C. Bahr and N. Ahmidi

Early-, Late-, and Very Late-Term Prediction of Target Lesion Failure in Coronary Artery Stent Patients: An International Multi-Site Study

2021 Frontiers in Artificial Intelligence, section Medicine and Public Health

H. Loureiro, T. Becker, A. Bauer-Mehren, N. Ahmidi, Janick Weberpals

Artificial Intelligence for Prognostic Scores in Oncology: a benchmarking study

2020 International Society for Pharmacoepidemiology (ISPE)

H. Loureiro, T. Becker, A. Bauer-Mehren, N. Ahmidi, J. Weberpals