NeoTriFuse: Reliability-Aware Multimodal Fusion under Missingness Heterogeneity for Neonatal Mortality Risk Prediction
Read the original on arXiv Machine Learning →NeoTriFuse is a reliability‑aware multimodal fusion framework designed to predict neonatal mortality risk from bedside monitoring data that suffers from extreme class imbalance, heterogeneous risk factors, multi‑scale temporal dynamics, and significant missingness. The method treats missing data as an explicit reliability signal, dynamically adjusting modality contributions during fusion through gating mechanisms that incorporate static perinatal variables, local‑global temporal encoders, and patient‑level statistical summaries. NeoTriFuse achieves competitive performance (F1 ≈ 0.674, AUROC ≈ 0.945) and ablation studies show that its temporal architecture and patient‑level summary branch are key contributors, with reliability‑aware gating further improving threshold‑dependent metrics under heterogeneous observation completeness.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.