Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Fahey, Carolyn A., Wei, Linqing, Njau, Prosper F., Shabani, Siraji, Kwilasa, Sylvester, Maokola, Werner, Packel, Laura, Zheng, Zeyu, Wang, Jingshen, McCoy, Sandra I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
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
_version_ 1784908530025758720
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
work_keys_str_mv AT faheycarolyna machinelearningwithroutineelectronicmedicalrecorddatatoidentifypeopleathighriskofdisengagementfromhivcareintanzania
AT weilinqing machinelearningwithroutineelectronicmedicalrecorddatatoidentifypeopleathighriskofdisengagementfromhivcareintanzania
AT njauprosperf machinelearningwithroutineelectronicmedicalrecorddatatoidentifypeopleathighriskofdisengagementfromhivcareintanzania
AT shabanisiraji machinelearningwithroutineelectronicmedicalrecorddatatoidentifypeopleathighriskofdisengagementfromhivcareintanzania
AT kwilasasylvester machinelearningwithroutineelectronicmedicalrecorddatatoidentifypeopleathighriskofdisengagementfromhivcareintanzania
AT maokolawerner machinelearningwithroutineelectronicmedicalrecorddatatoidentifypeopleathighriskofdisengagementfromhivcareintanzania
AT packellaura machinelearningwithroutineelectronicmedicalrecorddatatoidentifypeopleathighriskofdisengagementfromhivcareintanzania
AT zhengzeyu machinelearningwithroutineelectronicmedicalrecorddatatoidentifypeopleathighriskofdisengagementfromhivcareintanzania
AT wangjingshen machinelearningwithroutineelectronicmedicalrecorddatatoidentifypeopleathighriskofdisengagementfromhivcareintanzania
AT mccoysandrai machinelearningwithroutineelectronicmedicalrecorddatatoidentifypeopleathighriskofdisengagementfromhivcareintanzania