Towards Quantification of Bias in Machine Learning for Healthcare: A Case Study of Renal Failure Prediction

Departments of Population Health and Radiology Center for Data Science New Y ork University Langone Medical Center Abstract 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 MLdriven models. 1 Introduction Data-driven models have become more common in the U.S. healthcare field as their use in clinical operations and diagnosing procedures have expanded exponentially. The ever-increasing processing power of machine-learning algorithms allows automatic analysis of huge quantities of data, theoretically maximizing the efficiency and accuracy of the medical diagnosing process. Predictions from machine-learning models already drive important healthcare decisions for over 70 million people across the United States[7].