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Protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the COVID-19 pandemic
INTRODUCTION: Sustained viral suppression, an indicator of long-term treatment success and mortality reduction, is one of four strategic areas of the ‘Ending the HIV Epidemic’ federal campaign launched in 2019. Under-represented populations, like racial or ethnic minority populations, sexual and gen...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10186088/ https://www.ncbi.nlm.nih.gov/pubmed/37188476 http://dx.doi.org/10.1136/bmjopen-2022-070869 |
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author | Zhang, Jiajia Yang, Xueying Weissman, Sharon Li, Xiaoming Olatosi, Bankole |
author_facet | Zhang, Jiajia Yang, Xueying Weissman, Sharon Li, Xiaoming Olatosi, Bankole |
author_sort | Zhang, Jiajia |
collection | PubMed |
description | INTRODUCTION: Sustained viral suppression, an indicator of long-term treatment success and mortality reduction, is one of four strategic areas of the ‘Ending the HIV Epidemic’ federal campaign launched in 2019. Under-represented populations, like racial or ethnic minority populations, sexual and gender minority groups, and socioeconomically disadvantaged populations, are disproportionately affected by HIV and experience a more striking virological failure. The COVID-19 pandemic might magnify the risk of incomplete viral suppression among under-represented people living with HIV (PLWH) due to interruptions in healthcare access and other worsened socioeconomic and environmental conditions. However, biomedical research rarely includes under-represented populations, resulting in biased algorithms. This proposal targets a broadly defined under-represented HIV population. It aims to develop a personalised viral suppression prediction model using machine learning (ML) techniques by incorporating multilevel factors using All of Us (AoU) data. METHODS AND ANALYSIS: This cohort study will use data from the AoU research programme, which aims to recruit a broad, diverse group of US populations historically under-represented in biomedical research. The programme harmonises data from multiple sources on an ongoing basis. It has recruited ~4800 PLWH with a series of self-reported survey data (eg, Lifestyle, Healthcare Access, COVID-19 Participant Experience) and relevant longitudinal electronic health records data. We will examine the change in viral suppression and develop personalised viral suppression prediction due to the impact of the COVID-19 pandemic using ML techniques, such as tree-based classifiers (classification and regression trees, random forest, decision tree and eXtreme Gradient Boosting), support vector machine, naïve Bayes and long short-term memory. ETHICS AND DISSEMINATION: The institutional review board approved the study at the University of South Carolina (Pro00124806) as a Non-Human Subject study. Findings will be published in peer-reviewed journals and disseminated at national and international conferences and through social media. |
format | Online Article Text |
id | pubmed-10186088 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-101860882023-05-16 Protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the COVID-19 pandemic Zhang, Jiajia Yang, Xueying Weissman, Sharon Li, Xiaoming Olatosi, Bankole BMJ Open HIV/AIDS INTRODUCTION: Sustained viral suppression, an indicator of long-term treatment success and mortality reduction, is one of four strategic areas of the ‘Ending the HIV Epidemic’ federal campaign launched in 2019. Under-represented populations, like racial or ethnic minority populations, sexual and gender minority groups, and socioeconomically disadvantaged populations, are disproportionately affected by HIV and experience a more striking virological failure. The COVID-19 pandemic might magnify the risk of incomplete viral suppression among under-represented people living with HIV (PLWH) due to interruptions in healthcare access and other worsened socioeconomic and environmental conditions. However, biomedical research rarely includes under-represented populations, resulting in biased algorithms. This proposal targets a broadly defined under-represented HIV population. It aims to develop a personalised viral suppression prediction model using machine learning (ML) techniques by incorporating multilevel factors using All of Us (AoU) data. METHODS AND ANALYSIS: This cohort study will use data from the AoU research programme, which aims to recruit a broad, diverse group of US populations historically under-represented in biomedical research. The programme harmonises data from multiple sources on an ongoing basis. It has recruited ~4800 PLWH with a series of self-reported survey data (eg, Lifestyle, Healthcare Access, COVID-19 Participant Experience) and relevant longitudinal electronic health records data. We will examine the change in viral suppression and develop personalised viral suppression prediction due to the impact of the COVID-19 pandemic using ML techniques, such as tree-based classifiers (classification and regression trees, random forest, decision tree and eXtreme Gradient Boosting), support vector machine, naïve Bayes and long short-term memory. ETHICS AND DISSEMINATION: The institutional review board approved the study at the University of South Carolina (Pro00124806) as a Non-Human Subject study. Findings will be published in peer-reviewed journals and disseminated at national and international conferences and through social media. BMJ Publishing Group 2023-05-15 /pmc/articles/PMC10186088/ /pubmed/37188476 http://dx.doi.org/10.1136/bmjopen-2022-070869 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | HIV/AIDS Zhang, Jiajia Yang, Xueying Weissman, Sharon Li, Xiaoming Olatosi, Bankole Protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the COVID-19 pandemic |
title | Protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the COVID-19 pandemic |
title_full | Protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the COVID-19 pandemic |
title_fullStr | Protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the COVID-19 pandemic |
title_full_unstemmed | Protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the COVID-19 pandemic |
title_short | Protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the COVID-19 pandemic |
title_sort | protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the covid-19 pandemic |
topic | HIV/AIDS |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10186088/ https://www.ncbi.nlm.nih.gov/pubmed/37188476 http://dx.doi.org/10.1136/bmjopen-2022-070869 |
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