Machine Learning Could Help Predict Adherence to HIV Treatment

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 ages 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 were published online Feb. 25, 2025, in AIDS.

“I have great interest in machine learning and want to apply it to problems that speak directly to me,” she said. “The collaborations betweeinn 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 dataset included adolescents between age 10-16 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.

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 the 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 these data and build tools to enhance health outcomes.” 

(Newswise/MS)