Overcoming Dengue Underreporting: A Machine Learning Framework for Automated Dengue Notification Based on Electronic Medical Records

Hospital notification of dengue cases is frequently delayed, contributing to underreporting. At Thai provincial hospitals the bottleneck is administrative, not diagnostic: delayed discharge coding and clerical steps mean notifications are submitted late or omitted. Existing machine learning (ML) studies require clinician-annotated features absent in passive electronic medical records (EMRs). We propose a dual-pathway EMR framework for ML-based pre-population of surveillance reports. We analyzed 1031 dengue-suspected encounters (508 notifiable) from Surin Hospital, Thailand (2019–2022). Pathway A combines EMR data with manually documented clinical features; Pathway B uses only passive EMR data. Using an 80/20 stratified split, we tested 7 ML methods across 11 feature sets (77 combinations) with SHAP-guided feature elimination and 10-fold cross-validation. On the primary split, Pathway A (CatBoost) achieved an F1 score of 96.62%, 95.10% sensitivity, and 98.10% specificity (10-seed mean F1 95.7% ± 1.4%). Pathway B (LightGBM) achieved an F1 score of 84.42%, 94.12% sensitivity, 75.24% specificity, and a projected negative predictive value of 99.1% at 10% prevalence; 10-seed performance was lower (F1 78.5% ± 4.3%; sensitivity 89.1%). An exploratory LSTM-16 stacking model (F1 83.99%; precision 79.49%) performed best in precision and specificity among Pathway B. Test F1 SD across 10 seeds was ≤5.0% in 75 of 77 combinations. Within this cohort, passive EMR modeling achieved 91–94% sensitivity on the primary split (85–89% across seeds) without additional data collection, supporting the feasibility of ML-based notification in resource-constrained hospitals. Whether this reduces dengue underreporting must be established in prospective, multi-center studies.