Nationwide EHR-Based Chronic Rhinosinusitis Prediction Using Demographic-Stratified Models

TL;DR

This study develops demographic-stratified models using nationwide EHR data to predict chronic rhinosinusitis, achieving high accuracy and aiding early diagnosis and triage in primary care.

Contribution

It introduces a hybrid feature-selection pipeline and demographic-specific models to improve CRS prediction from large-scale EHR data.

Findings

Achieved an overall AUC of 0.8461 in CRS prediction.

Reduced approximately 110,000 codes to 100 interpretable features.

Demographic stratification improved model discrimination.

Abstract

Chronic rhinosinusitis (CRS) is a common heterogeneous inflammatory disorder that causes substantial morbidity and healthcare costs. CRS is difficult to identify early from routine encounters, as symptom presentations overlap with common conditions such as allergic rhinitis, and heterogeneous phenotypes further obscure risk patterns. Prior predictive studies often rely on single-institutional cohorts , which reduce population-level generalizability. To overcome this, we leveraged nationwide longitudinal EHR data from the \textit{All of Us} Research Program to predict CRS diagnosis using two years of pre-diagnostic history. To address extreme feature sparsity and dimensionality in coded EHR data, we implemented a hybrid feature-selection pipeline that combines prevalence-based statistical screening with model-based importance ranking, compressing approximately 110,000 candidate codes into 100 interpretable features. To capture demographic heterogeneity, we trained demographic stratified models across six adult sex and life-stage subgroups with subgroup-specific hyperparameter tuning. Our framework achieved an overall AUC of 0.8461, improving discrimination by 0.0168 over the best baseline. These results demonstrate that routinely collected EHR data may support population-representative CRS risk stratification and inform earlier triage and referral prioritization in primary care.