Subpopulation-specific machine learning prognosis for underrepresented patients with double prioritized bias correction.

ABSTRACT:

Background

Many clinical datasets are intrinsically imbalanced, dominated by overwhelming majority groups. Off-the-shelf machine learning models that optimize the prognosis of majority patient types (e.g., healthy class) may cause substantial errors on the minority prediction class (e.g., disease class) and demographic subgroups (e.g., Black or young patients). In the typical one-machine-learning-model-fits-all paradigm, racial and age disparities are likely to exist, but unreported. In addition, some widely used whole-population metrics give misleading results.

Methods

We design a double prioritized (DP) bias correction technique to mitigate representational biases in machine learning-based prognosis. Our method trains customized machine learning models for specific ethnici

SUBMITTER: Afrose S 

PROVIDER: S-EPMC9436942 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature