A group of authors from Duke, including AI Health Data Science Fellow Elliot Hill and Data Science Fellowship Director Matthew Engelhard, published a research article that examined the propensity for a supervised machine learning approach known as binary classification to yield biased results when predicting long-horizon diagnoses. The paper, titled “Limitations of Binary Classification for Long-Horizon Diagnosis Prediction and Advantages of a Discrete-Time Time-to-Event Approach: Empirical Analysis,” was published in March in the journal JMIR AI.
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