Taking race into account when developing tools to predict a patient's risk of colorectal cancer leads to more accurate predictions when compared with race-blind algorithms, researchers find. While many medical researchers have argued that race should be removed as a factor from clinical algorithms that predict disease risks, a new study finds that, at least for colorectal cancer, including race can help correct a data issue—inaccurate recording of family history for Black patients. Having relatives with colorectal cancer is a known risk factor for the disease, but Black patients are less likely to have an accurate recorded history in their medical records.
Considering race can help correct for this, potentially identifying more Black patients who would benefit from cancer screening. "If you don't use race, what you're effectively doing is you're telling your algorithm , pretend that family history is equally useful for everyone, and that's just not true in the data," said Emma Pierson, senior author on the new study and 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. She collaborated with Anna Zink of the University of Chicago and Ziad Obermeyer of the University of California, Berkeley on the new research, "Race Adjustments in Clinical Algorithms Can Help Correct For Racial Disparities In Data Quality," which was pub.