Machine Learning-Based HIV Risk Estimation Using Incidence Rate Ratios

Machine Learning-Based HIV Risk Estimation Using Incidence Rate Ratios

HIV/AIDS is an ongoing global pandemic, with an estimated 39 million infected worldwide. Early detection is anticipated to help improve outcomes and prevent further infections. Point-of-care diagnostics make HIV/AIDS diagnoses available both earlier and to a broader population. Wide-spread and autom...

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Bibliographic Details
Main Authors: Oliver Haas, Andreas Maier, Eva Rothgang
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-12-01
Series:Frontiers in Reproductive Health
Subjects:
HIV
risk estimation
association rules
bias
clinical data
machine learning
Online Access:https://www.frontiersin.org/articles/10.3389/frph.2021.756405/full

Internet

https://www.frontiersin.org/articles/10.3389/frph.2021.756405/full

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