Kaiser Permanente develops machine learning tool to predict HIV risk

Researchers at Kaiser Permanente developed a machine learning algorithm officials say could ultimately help prevent HIV transmission.

The analytical tool more effectively identifies people at risk of contracting HIV compared to other HIV risk prediction tools to enable more at-risk patients to be referred for preventive medication, according to a study describing the prediction tool published July 5 in The Lancet HIV.

Investigators at Kaiser Permanente San Francisco, the Kaiser Permanente Division of Research, Beth Israel Deaconess Medical Center, and Harvard Medical School analyzed medical records of 3.7 million Kaiser Permanente patients and developed a machine learning algorithm to predict who would become infected with HIV during a three-year period.