Learning Disease Progression Models That Capture Health Disparities
Journal:
arXiv
Published Date:
Dec 20, 2024
Abstract
Disease progression models are widely used to inform the diagnosis and
treatment of many progressive diseases. However, a significant limitation of
existing models is that they do not account for health disparities that can
bias the observed data. To address this, we develop an interpretable Bayesian
disease progression model that captures three key health disparities: certain
patient populations may (1) start receiving care only when their disease is
more severe, (2) experience faster disease progression even while receiving
care, or (3) receive follow-up care less frequently conditional on disease
severity. We show theoretically and empirically that failing to account for any
of these disparities can result in biased estimates of severity (e.g.,
underestimating severity for disadvantaged groups). On a dataset of heart
failure patients, we show that our model can identify groups that face each
type of health disparity, and that accounting for these disparities while
inferring disease severity meaningfully shifts which patients are considered
high-risk.
