In a new research article titled “Using mixture cure models to address algorithmic bias in diagnostic timing: autism as a test case,” group of Duke authors, including AI Health Director of Data Science Ben Goldstein, PhD, and Data Science Fellowship Director Matthew Engelhard, PhD, examine algorithmic approaches for models used to predict autism diagnosis. The simulation study, which is published in the journal JAMIA Open, suggests that mixture cure models show promise in improving predictive modeling in autism and potentially other conditions.
