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

ObjectivesThe 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. We address this over...

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Vydáno v:	BMJ health & care informatics Ročník 29; číslo 1; s. e100457
Hlavní autoři:	Straw, Isabel, Wu, Honghan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England BMJ Publishing Group Ltd 01.04.2022 BMJ Publishing Group LTD BMJ Publishing Group
Témata:	Algorithms Artificial Intelligence Bayes Theorem Bias Biomarkers BMJ Health Informatics Delivery of Health Care Female Gender differences Health Equity Health informatics Humans Liver Diseases Machine Learning Male Original Research Public health informatics Supervised Machine Learning United Kingdom > UK Public health informatics BMJ Health Informatics Health Equity Artificial intelligence Machine Learning
ISSN:	2632-1009, 2632-1009
On-line přístup:	Získat plný text
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