[2605.05213] Nationwide EHR-Based Chronic Rhinosinusitis Prediction Using Demographic-Stratified Models

Computer Science > Machine Learning arXiv:2605.05213 (cs) [Submitted on 16 Apr 2026] Title:Nationwide EHR-Based Chronic Rhinosinusitis Prediction Using Demographic-Stratified Models Authors:Sicong Chang, Yidan Shen, Justina Varghese, Akshay R Prabhakar, Sebastian Guadarrama-Sistos-Vazquez, Jiefu Chen, Masayoshi Takashima, Omar G. Ahmed, Renjie Hu, Xin Fu View a PDF of the paper titled Nationwide EHR-Based Chronic Rhinosinusitis Prediction Using Demographic-Stratified Models, by Sicong Chang and 8 other authors View PDF HTML (experimental) 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 dem...