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Machine learning with routine electronic medical record data to identify people at high risk of disengagement from HIV care in Tanzania
Machine learning methods for health care delivery optimization have the potential to improve retention in HIV care, a critical target of global efforts to end the epidemic. However, these methods have not been widely applied to medical record data in low- and middle-income countries. We used an ense...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10021592/ https://www.ncbi.nlm.nih.gov/pubmed/36962586 http://dx.doi.org/10.1371/journal.pgph.0000720 |
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author | Fahey, Carolyn A. Wei, Linqing Njau, Prosper F. Shabani, Siraji Kwilasa, Sylvester Maokola, Werner Packel, Laura Zheng, Zeyu Wang, Jingshen McCoy, Sandra I. |
author_facet | Fahey, Carolyn A. Wei, Linqing Njau, Prosper F. Shabani, Siraji Kwilasa, Sylvester Maokola, Werner Packel, Laura Zheng, Zeyu Wang, Jingshen McCoy, Sandra I. |
author_sort | Fahey, Carolyn A. |
collection | PubMed |
description | Machine learning methods for health care delivery optimization have the potential to improve retention in HIV care, a critical target of global efforts to end the epidemic. However, these methods have not been widely applied to medical record data in low- and middle-income countries. We used an ensemble decision tree approach to predict risk of disengagement from HIV care (missing an appointment by ≥28 days) in Tanzania. Our approach used routine electronic medical records (EMR) from the time of antiretroviral therapy (ART) initiation through 24 months of follow-up for 178 adults (63% female). We compared prediction accuracy when using EMR-based predictors alone and in combination with sociodemographic survey data collected by a research study. Models that included only EMR-based indicators and incorporated changes across past clinical visits achieved a mean accuracy of 75.2% for predicting risk of disengagement in the next 6 months, with a mean sensitivity of 54.7% for targeting the 30% highest-risk individuals. Additionally including survey-based predictors only modestly improved model performance. The most important variables for prediction were time-varying EMR indicators including changes in treatment status, body weight, and WHO clinical stage. Machine learning methods applied to existing EMR data in resource-constrained settings can predict individuals’ future risk of disengagement from HIV care, potentially enabling better targeting and efficiency of interventions to promote retention in care. |
format | Online Article Text |
id | pubmed-10021592 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-100215922023-03-17 Machine learning with routine electronic medical record data to identify people at high risk of disengagement from HIV care in Tanzania Fahey, Carolyn A. Wei, Linqing Njau, Prosper F. Shabani, Siraji Kwilasa, Sylvester Maokola, Werner Packel, Laura Zheng, Zeyu Wang, Jingshen McCoy, Sandra I. PLOS Glob Public Health Research Article Machine learning methods for health care delivery optimization have the potential to improve retention in HIV care, a critical target of global efforts to end the epidemic. However, these methods have not been widely applied to medical record data in low- and middle-income countries. We used an ensemble decision tree approach to predict risk of disengagement from HIV care (missing an appointment by ≥28 days) in Tanzania. Our approach used routine electronic medical records (EMR) from the time of antiretroviral therapy (ART) initiation through 24 months of follow-up for 178 adults (63% female). We compared prediction accuracy when using EMR-based predictors alone and in combination with sociodemographic survey data collected by a research study. Models that included only EMR-based indicators and incorporated changes across past clinical visits achieved a mean accuracy of 75.2% for predicting risk of disengagement in the next 6 months, with a mean sensitivity of 54.7% for targeting the 30% highest-risk individuals. Additionally including survey-based predictors only modestly improved model performance. The most important variables for prediction were time-varying EMR indicators including changes in treatment status, body weight, and WHO clinical stage. Machine learning methods applied to existing EMR data in resource-constrained settings can predict individuals’ future risk of disengagement from HIV care, potentially enabling better targeting and efficiency of interventions to promote retention in care. Public Library of Science 2022-09-16 /pmc/articles/PMC10021592/ /pubmed/36962586 http://dx.doi.org/10.1371/journal.pgph.0000720 Text en © 2022 Fahey et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Fahey, Carolyn A. Wei, Linqing Njau, Prosper F. Shabani, Siraji Kwilasa, Sylvester Maokola, Werner Packel, Laura Zheng, Zeyu Wang, Jingshen McCoy, Sandra I. Machine learning with routine electronic medical record data to identify people at high risk of disengagement from HIV care in Tanzania |
title | Machine learning with routine electronic medical record data to identify people at high risk of disengagement from HIV care in Tanzania |
title_full | Machine learning with routine electronic medical record data to identify people at high risk of disengagement from HIV care in Tanzania |
title_fullStr | Machine learning with routine electronic medical record data to identify people at high risk of disengagement from HIV care in Tanzania |
title_full_unstemmed | Machine learning with routine electronic medical record data to identify people at high risk of disengagement from HIV care in Tanzania |
title_short | Machine learning with routine electronic medical record data to identify people at high risk of disengagement from HIV care in Tanzania |
title_sort | machine learning with routine electronic medical record data to identify people at high risk of disengagement from hiv care in tanzania |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10021592/ https://www.ncbi.nlm.nih.gov/pubmed/36962586 http://dx.doi.org/10.1371/journal.pgph.0000720 |
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