Investigating for bias in healthcare algorithms: a sex-stratified analysis of supervised machine learning models in liver disease prediction (25 April 2022)

Summary

The Indian Liver Patient Dataset (ILPD) is used extensively to create algorithms that predict liver disease. Given the existing research describing demographic inequities in liver disease diagnosis and management, these algorithms require scrutiny for potential biases. Isabel Straw and Honghan Wu address this overlooked issue by investigating ILPD models for sex bias.

They demonstrated a sex disparity that exists in published ILPD classifiers. In practice, the higher false negative rate for females would manifest as increased rates of missed diagnosis for female patients and a consequent lack of appropriate care. Our study demonstrates that evaluating biases in the initial stages of machine learning can provide insights into inequalities in current clinical practice, reveal pathophysiological differences between the male and females, and can mitigate the digitisation of inequalities into algorithmic systems.

An awareness of the potential biases of these systems is essential in preventing the digital exacerbation of healthcare inequalities.