Intimate partner violence is notoriously underreported and correctly diagnosed at hospitals only around a quarter of the time, but a new method provides a more realistic picture of which groups of women are most affected, even when their cases go unrecorded. PURPLE, an algorithm developed by researchers at Cornell and the Massachusetts Institute of Technology, estimates how often underreported health conditions occur in different demographic groups. Using hospital data, the researchers showed that PURPLE can better quantify which groups of women are most likely to experience intimate partner violence compared with methods that do not correct for underreporting.
The new method was developed by Divya Shanmugam, formerly a doctoral student at MIT who will join Cornell Tech as a postdoctoral researcher this fall, and Emma Pierson, the Andrew H. and Ann R. Tisch Assistant Professor of computer science at the Jacobs Technion-Cornell Institute at Cornell Tech and in the Cornell Ann S.
Bowers College of Computing and Information Science. The researchers describe their approach in "Quantifying Disparities in Intimate Partner Violence: a Machine Learning Method to Correct for Underreporting," published in npj Women's Health . "Often we care about how commonly a disease occurs in one population versus another, because it can help us target resources to the groups who need it most," Pierson said.
"The challenge is, many diseases are underdiagnosed. Underreporting is intimately bound up w.