A Multimodal Early-Warning Framework for Respiratory Exacerbation Prediction via Acoustic, Genomic, and Environmental Data Fusion

Overview

Respiratory exacerbations are often detected only after symptoms become severe, limiting opportunities for early intervention. Current monitoring systems rely on clinic-based devices or single-modality data, restricting scalability and predictive accuracy. This project developed and validated a unified multimodal computational framework integrating bioacoustic cough analysis, population-level genomic risk modeling, and environmental context for early respiratory risk forecasting. The system includes two primary pipelines: (1) a bioacoustic time-series model extracting spectral and temporal features from cough audio using a CNN–BiLSTM–Attention architecture, and (2) a genomic module incorporating 42 high-impact SNPs from GWAS summary statistics to generate probabilistic population-level risk weights. Outputs were fused through a time-aware multimodal integration layer optimized for early event detection. Models were trained and validated on 310,000 cough-audio segments and 18,200 aggregated genomic risk records from independent de-identified datasets. Cross-dataset validation (COUGHVID to Coswara) achieved an AUROC of 0.93 (95% CI: 0.91–0.95) and AUPRC of 0.88. Compared to an acoustic-only baseline (AUROC 0.86), multimodal fusion significantly improved performance (p < 0.01). Calibration showed strong agreement between predicted and observed risk (Brier score 0.11). In longitudinal simulations, detection lead time increased from 1.2 to 3.1 days (+1.9 days) while maintaining a 5.8% false alarm rate. These results demonstrate that multimodal integration improves predictive accuracy and early-warning capability, supporting scalable, in-silico respiratory risk forecasting for remote and resource-limited settings.

Competition history

• ISEF 2026 Biomedical Engineering · Entry ENBM068

Resources

• ISEF abstract page ↗

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