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Published in Circulation: Heart Failure, a recent study conducted by Northwestern Medicine researchers found that adding social determinants of health (SDOH) data into machine learning models led to less bias when predicting outcomes for heart failure patients.
Healthcare bias can lead to detrimental outcomes for patients. For example, in April, a study published in PLOS digital health found that digital health biases and data gaps involved in the production of artificial intelligence (AI) tools can lead to healthcare disparities.
But Northwestern Medicine researchers have found that SDOH data can help curb bias, thereby reducing disparities.
The study included performance reviews of five machine-learning models often used to determine the outcomes of hospitalized patients. They then integrated 15 measures of SDOH, all of which were provided by the American Heart Association, into these machine-learning models. These measures included Social Deprivation Index scores and Area Deprivation Index scores.