  Retaining persons living with HIV (PLWH) in medical care is paramount to
preventing new transmissions of the virus and allowing PLWH to live normal and
healthy lifespans. Maintaining regular appointments with an HIV provider and
taking medication daily for a lifetime is exceedingly difficult. 51% of PLWH
are non-adherent with their medications and eventually drop out of medical
care. Current methods of re-linking individuals to care are reactive (after a
patient has dropped-out) and hence not very effective. We describe our system
to predict who is most at risk to drop-out-of-care for use by the University of
Chicago HIV clinic and the Chicago Department of Public Health. Models were
selected based on their predictive performance under resource constraints,
stability over time, as well as fairness. Our system is applicable as a
point-of-care system in a clinical setting as well as a batch prediction system
to support regular interventions at the city level. Our model performs 3x
better than the baseline for the clinical model and 2.3x better than baseline
for the city-wide model. The code has been released on github and we hope this
methodology, particularly our focus on fairness, will be adopted by other
clinics and public health agencies in order to curb the HIV epidemic.
