[2605.05213] Nationwide EHR-Based Chronic Rhinosinusitis Prediction Using Demographic-Stratified Models
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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
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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.
Comments: Sicong Chang, Yidan Shen are the co-first authors This paper is already accepted to IEEE Engineering in Medicine and Biology Society (EMBC) 2026 conference
Subjects:
Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
Cite as: arXiv:2605.05213 [cs.LG]
(or arXiv:2605.05213v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.05213
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arXiv-issued DOI via DataCite
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From: Yidan Shen [view email] [v1] Thu, 16 Apr 2026 21:27:05 UTC (1,556 KB)
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