Pilot Projects


Predicting renal recovery from acute kidney injury using artificial intelligence

Principal investigators

Dr. med.

Thilo von Groote

Univ.-Prof. Dr. med.

Julian Varghese


Acute kidney injury (AKI) is a frequent and severe complication in critically ill patients, and is especially associated with systemic inflammatory syndromes. AKI leads to increased morbidity and mortality and has implications both on short- and long-term outcomes, such as chronic kidney disease (CKD). Renal recovery from AKI is a key factor on the AKI-to-CKD-continuum, however clinical prediction of renal recovery is difficult, and the scientific understanding of these processes is still very limited. 

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Increasingly, methods of artificial intelligence and machine learning are applied to clinical research projects, including AKI. However, so far, no machine-learning models have been developed for the prediction of renal recovery from AKI.

This project aims to establish such prediction models, supporting clinicians in their bedside decision-making in this regard. Furthermore, we aim to apply causal modelling approaches and interpretable machine-learning algorithms to generate new knowledge about the pathophysiology of renal recovery from AKI. To enable these data-driven models, we have established a research-compatible data-warehouse which is based on the AMDS dataset and integrates state-of-the-art machine learning analyses and data visualisation tools.

Project Team

Dr. rer. nat.

Christian Porschen

Dr. rer. nat.

Michael Fujarski

PD Dr. med.

Narges Ghoreishi