Work

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

Year: 2019

Type: preprint

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 ML-driven models.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1911.07679

Authors: Williams, Josie, Razavian, Narges

Institutions: Courant Institute of Mathematical Sciences, New York University, NYU Langone Health

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: green

OpenAlex ID: W2985132472

Cites: 8

Cited by: 2

Related to: 10