by team Zdravíme
- IKEM prevalence data
- High ACR risk predictive model
Based on the medical records, we can give a doctor the risk of a particular patient being in the A2 and A3 CKD risk categories. Because the albuminuria is not standardly tested, we can recommend such action to the doctor when actually needed!
- data/ - placeholder
- src/ - our solution
- presentation/ - some outcomes of our work
- we developed our solutions in Python Jupyter notebooks and Julia.
- from analytical tools, we relied mostly on statsmodels library and our wit; we also tested causal inference tools and advanced ideas like sum-product-networks for sum-clever-analysis.
Our final solution builds on a two-level approach. First, by estimating the availability of ACR testing results based on other covariates, we understand the sampling bias of the data. Then, we can properly learn a model predicting the ACR levels for a particular patient and adjusting by inverse probability weighting for the sampling bias. Given the estimated ACR levels and their uncertainty, we give the doctor a percentual risk of the patient being above the defined thresholds of the A2 and A3 CKD-albuminuria-based category.