Machine learning could help predict adherence to HIV treatment

Graduate student Claire Najjuuko has worked 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.

Empowering empathy with AI: The Moving Stories project

Dr. Ariela Schachter and her faculty collaborators had an idea. As an associate professor of sociology, she wanted to create an app, together with Dr. Ila Sheren and Dr. Tabea Linhard, to study what happens when you connect members of the St. Louis community to their neighbors with migrant backgrounds.