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.
0 Comments
Recommended Comments
There are no comments to display.
Create an account or sign in to comment
You need to be a member in order to leave a comment
Create an account
Sign up for a new account in our community. It's easy!
Register a new accountSign in
Already have an account? Sign in here.
Sign In Now