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

18 Nov 2019Josie WilliamsNarges Razavian

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... (read more)

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