Beyond the One-Size-Fits-All: A Deep Learning Method for Equitable Data Integration and Subgroup-Specific Biomarker Identification- An Application to COPD

The heterogeneity of chronic obstructive pulmonary disease (COPD) and other complex diseases has spurred efforts to leverage multiomics and phenotypic data to identify biomarkers of disease risk and progression, to better understand the underlying physiology. These attempts focus mainly on the general population, use few molecular factors, hardly account for social determinants of health (SDoH), and establish simple associations, limiting ability to better characterize health for disadvantaged populations. We propose a broader, systems level perspective centered on the totality of SDoH, multiomics, and phenotypic data, using innovative interpretable deep learning (DL) methods to better understand and help address health disparities in COPD and other complex diseases. Our proposed DL method jointly integrates data from multiple sources and predicts a clinical outcome while yielding common and subgroup-specific variable selection and encouraging fairness with respect to sensitive attributes (e.g.,race). Simulations are used to demonstrate the effectiveness of the proposed and other methods in the literature. Real data analyses are conducted to identify race- specific multiomics markers of COPD. 

Interpretable Deep Learning

Fairness

Multimodal Data Integration

Health Disparities