| Autor: |
Carolyn A Fahey, Linqing Wei, Prosper F Njau, Siraji Shabani, Sylvester Kwilasa, Werner Maokola, Laura Packel, Zeyu Zheng, Jingshen Wang, Sandra I McCoy |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
PLOS Global Public Health, Vol 2, Iss 9, p e0000720 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2767-3375 |
| DOI: |
10.1371/journal.pgph.0000720 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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