Machine learning could help predict adherence to HIV treatment in adolescents

Nearly 85% of the 1.7 million adolescents with HIV live in sub-Saharan Africa, along with half of the nearly 40 million people in the world living with HIV. Although the government in Uganda provides antiretroviral treatment (ART) for free, adherence to the regimen by adolescents aged 10–16 is low, increasing the potential for the virus to further spread.

Claire Najjuuko, a doctoral student at Washington University in St. Louis, saw this firsthand while working as a data manager at the International Center for Child Health and Development (ICHAD) in Uganda, founded by Fred M. Ssewamala, the William E. Gordon Distinguished Professor in the Brown School at WashU.

Now earning a doctorate in WashU’s Division of Computational & Data Sciences, Najjuuko, who is co-advised by Ssewamala and Chenyang Lu, the Fullgraf Professor in the Department of Computer Science & Engineering in the McKelvey School of Engineering, wanted to use artificial intelligence and data science to help improve adolescent compliance with the treatment in low-resource areas.

Results of the research appear in the journal AIDS.

“I have great interest in machine learning and want to apply it to problems that speak directly to me,” she said. “The collaborations between the AI for Health Institute directed by Professor Lu and the International Center for Child Health and Development directed by Professor Fred are particularly enabling this kind of innovative work.”

With support from Lu and Ssewamala, Najjuuko set out to develop a machine learning model to predict which adolescents with HIV would be less likely to adhere to antiretroviral therapy. With such knowledge, health care practitioners could implement interventions for those identified as less likely to adhere to the treatment plan.

“The current way the practice is, adolescents go to the clinic every month or two months for medication refills, and a health care practitioner checks how many pills the patient has left compared with what is expected, as well as asking the adolescent questions regarding missed doses to establish if the patient is adhering to the therapy,” Najjuuko said. “This project to predict future nonadherence of adolescents can have real impact if implemented in the right way.”

To train the model, Najjuuko used data from a six-year cluster-randomized controlled trial from 39 clinics in southern Uganda, a region most heavily impacted by HIV. The Suubi+Adherence dataset included adolescents between the ages of 10 and 16 medically diagnosed with HIV, aware of their status, enrolled in ART at one of the clinics and living within a family. Ultimately, the models analyzed data from 647 patients who had complete data on the outcome at 48 months.

Najjuuko developed a machine learning model to predict nonadherence to antiretroviral therapy by incorporating socio-behavioral and economic factors alongside a patient’s adherence history. The model accurately identifies 80% of adolescents at risk of nonadherence while lowering the false alarm rate to 52%, which is 14 percentage points lower than a model based solely on adherence history. By reducing false alarms, this model helps health care providers focus interventions on those who need them most, improving patient outcomes while reducing unnecessary follow-ups and provider fatigue.

Among 50 variables, which included social, interpersonal, family, educational, structural and economic factors, the model found 12 that were most predictive of an individual having poor adherence to ART. Economic factors were highly associated with future nonadherence. Other predictive characteristics were poor adherence history; child poverty; biological relationship to primary caregiver; self-concept; confidence in saving money; discussing sensitive topics with caregivers; household size; and school enrollment.

“Adolescents are the most nonadherent group across the globe,” Ssewamala said. “They are moving into independence and don’t want to be told what to do. As they move into the dating period, there is a lot of stigma, and they don’t want to be associated with HIV.”

One factor the team found was associated with adolescents with HIV adhering to the ART therapy was having a savings account.

“The theory is when people own resources, especially when they have a nest egg, they think and behave differently,” Ssewamala said. “The future holds promise, so they will take care of themselves so they can live longer. When people are hopeless, they have nothing to lose.”

Adhering to the treatment is difficult, Ssewamala said, because the medication must be taken with food or causes nausea. If a person with HIV doesn’t have access to food or transportation to get the medication, they are less likely to adhere to the treatment.

Lu said this model could be adapted for deployment in the field to support personalized intervention strategies based on the identified risk factors, highlighting the importance of collaboration.

“This is an excellent example of interdisciplinary research at WashU, combining AI and global health,” Lu said. “By leveraging the data that Fred’s team gathered from the field and their insights on complex health issues, we apply AI expertise to analyze this data and build tools to enhance health outcomes.”

Nearly 85% of the 1.7 million adolescents with HIV live in sub-Saharan Africa, along with half of the nearly 40 million people in the world living with HIV. Although the government in Uganda provides antiretroviral treatment (ART) for free, adherence to the regimen by adolescents aged 10–16 is low, increasing the potential for the virus to further spread.

Claire Najjuuko, a doctoral student at Washington University in St. Louis, saw this firsthand while working as a data manager at the International Center for Child Health and Development (ICHAD) in Uganda, founded by Fred M. Ssewamala, the William E. Gordon Distinguished Professor in the Brown School at WashU.

Now earning a doctorate in WashU’s Division of Computational & Data Sciences, Najjuuko, who is co-advised by Ssewamala and Chenyang Lu, the Fullgraf Professor in the Department of Computer Science & Engineering in the McKelvey School of Engineering, wanted to use artificial intelligence and data science to help improve adolescent compliance with the treatment in low-resource areas.

Results of the research appear in the journal AIDS.

“I have great interest in machine learning and want to apply it to problems that speak directly to me,” she said. “The collaborations between the AI for Health Institute directed by Professor Lu and the International Center for Child Health and Development directed by Professor Fred are particularly enabling this kind of innovative work.”

With support from Lu and Ssewamala, Najjuuko set out to develop a machine learning model to predict which adolescents with HIV would be less likely to adhere to antiretroviral therapy. With such knowledge, health care practitioners could implement interventions for those identified as less likely to adhere to the treatment plan.

“The current way the practice is, adolescents go to the clinic every month or two months for medication refills, and a health care practitioner checks how many pills the patient has left compared with what is expected, as well as asking the adolescent questions regarding missed doses to establish if the patient is adhering to the therapy,” Najjuuko said. “This project to predict future nonadherence of adolescents can have real impact if implemented in the right way.”

To train the model, Najjuuko used data from a six-year cluster-randomized controlled trial from 39 clinics in southern Uganda, a region most heavily impacted by HIV. The Suubi+Adherence dataset included adolescents between the ages of 10 and 16 medically diagnosed with HIV, aware of their status, enrolled in ART at one of the clinics and living within a family. Ultimately, the models analyzed data from 647 patients who had complete data on the outcome at 48 months.

Najjuuko developed a machine learning model to predict nonadherence to antiretroviral therapy by incorporating socio-behavioral and economic factors alongside a patient’s adherence history. The model accurately identifies 80% of adolescents at risk of nonadherence while lowering the false alarm rate to 52%, which is 14 percentage points lower than a model based solely on adherence history. By reducing false alarms, this model helps health care providers focus interventions on those who need them most, improving patient outcomes while reducing unnecessary follow-ups and provider fatigue.

Among 50 variables, which included social, interpersonal, family, educational, structural and economic factors, the model found 12 that were most predictive of an individual having poor adherence to ART. Economic factors were highly associated with future nonadherence. Other predictive characteristics were poor adherence history; child poverty; biological relationship to primary caregiver; self-concept; confidence in saving money; discussing sensitive topics with caregivers; household size; and school enrollment.

“Adolescents are the most nonadherent group across the globe,” Ssewamala said. “They are moving into independence and don’t want to be told what to do. As they move into the dating period, there is a lot of stigma, and they don’t want to be associated with HIV.”

One factor the team found was associated with adolescents with HIV adhering to the ART therapy was having a savings account.

“The theory is when people own resources, especially when they have a nest egg, they think and behave differently,” Ssewamala said. “The future holds promise, so they will take care of themselves so they can live longer. When people are hopeless, they have nothing to lose.”

Adhering to the treatment is difficult, Ssewamala said, because the medication must be taken with food or causes nausea. If a person with HIV doesn’t have access to food or transportation to get the medication, they are less likely to adhere to the treatment.

Lu said this model could be adapted for deployment in the field to support personalized intervention strategies based on the identified risk factors, highlighting the importance of collaboration.

“This is an excellent example of interdisciplinary research at WashU, combining AI and global health,” Lu said. “By leveraging the data that Fred’s team gathered from the field and their insights on complex health issues, we apply AI expertise to analyze this data and build tools to enhance health outcomes.”