  As machine learning (ML) models, trained on real-world datasets, become
common practice, it is critical to measure and quantify their potential biases.
In this paper, we focus on renal failure and compare a commonly used
traditional risk score, Tangri, with a more powerful machine learning model,
which has access to a larger variable set and trained on 1.6 million patients'
EHR data. We will compare and discuss the generalization and applicability of
these two models, in an attempt to quantify biases of status quo clinical
practice, compared to ML-driven models.
