SP 3 - CKDNapp
Algorithmic foundation of CKDNapp
One of the key ingredients of CKDNapp is the mathematical modeling of chronic kidney disease (CKD). We will build models that describe relations between clinically relevant variables, such as serum creatinine, blood pressure, and urinary protein concentration, as well as demographic factors and co-morbidities. These variables can be either continuous, e.g., the concentration of serum creatinine in blood, or categorical, e.g., sex. In this subproject, we will develop algorithms to estimate such complex relationships between variables of diverse data types. There, we will also develop strategies to better understand causal relationships between these variables.
Further, we will provide algorithms to facilitate the application of CKDNapp using low-cost measurement strategies. We will finally make our software solutions publicly available.