Using mixture cure models to address algorithmic bias in diagnostic timing: autism as a test case.

ABSTRACT:

Objectives

To address algorithmic bias in clinical prediction models related to the timing of diagnosis, we evaluated the efficacy of mixture cure models that integrate time-to-event and binary classification frameworks to predict diagnoses.

Materials and methods

We conducted a simulation and analyzed real-world North Carolina Medicaid data for children born in 2014, followed until 2023. The study evaluated traditional time-to-event and classification models against mixture cure models under scenarios with varied diagnostic timing and censoring.

Results

Simulation results demonstrated that traditional models exhibit increased bias as diagnosis timing differences widened, whereas mixture cure models yielded unbiased estimates across varying censoring times. In real-wor

SUBMITTER: Wu P 

PROVIDER: S-EPMC12598640 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature