Investigating Disparities in Machine Learning Algorithms - News Center

Integrating social determinants of health into machine learning models helped mitigate bias when predicting long-term outcomes for heart failure patients, according to a Northwestern Medicine study published in Circulation: Heart Failure. The study found that integrating 15 measures of social determinants of health into select machine learning models noticeably reduced disparities observed in predicting the probability of long-term hospitalization or in-hospital mortality for heart failure patients. "We show that for minority populations, the machine learning models actually performed worse than for white individuals. We also show that for people with poor socioeconomic status, let's say for those uninsured or for people that have Medicaid, the model also performed worse and missed people that are at a higher risk of dying or have a higher risk of staying in the hospital longer," said Yuan Luo, PhD, associate professor of Preventive Medicine, of Pediatrics, chief AI officer at the Northwestern Clinical and Translational Sciences (NUCATS) Institute and the Institute for Augmented Intelligence in Medicine, and senior author of the study. Machine learning can be a powerful tool for predicting long-term patient outcomes, especially for diagnosed with chronic conditions such as heart failure.