Fairness and bias correction in machine learning for depression prediction across four study populations

Fairness and bias correction in machine learning for depression prediction across four study populations

Abstract A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations. Inequalities are reflected in the data collected for scientific purposes. When not properly accounted for, machine learning (ML) models learned from data can reinforce these str...

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Bibliographic Details
Main Authors: Vien Ngoc Dang, Anna Cascarano, Rosa H. Mulder, Charlotte Cecil, Maria A. Zuluaga, Jerónimo Hernández-González, Karim Lekadir
Format: Article
Language:English
Published: Nature Portfolio 2024-04-01
Series:Scientific Reports
Subjects:
Machine learning for depression prediction
Algorithmic fairness
Bias mitigation
Novel post-hoc method
Psychiatric healthcare equity
Online Access:https://doi.org/10.1038/s41598-024-58427-7

Internet

https://doi.org/10.1038/s41598-024-58427-7

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