CERVINet-DELTA: Multimodal Forecasting for Labor Induction

# CERVINet-DELTA: Multimodal Time-Series Framework for Labor Induction Prediction > A robust deep learning framework for individualized prediction of labor outcomes induced by dinoprostone, using multimodal fusion and temporal attention modeling. ## 🧠 Overview **CERVINet-DELTA** is an interpretable and personalized predictive system that models the dynamic process of labor induction. It fuses structured clinical features, ultrasound imagery, and time-series uterine activity using attention-enhanced neural networks to forecast key outcomes such as: - Time to active labor- Total time to delivery- Delivery mode (vaginal, assisted, cesarean)- Neonatal Apgar scores This framework supports **real-time decision making**, **risk stratification**, and **policy optimization** based on individualized clinical profiles. ## 📦 Features - ⚙️ **CERVINet**: Stochastic latent dynamics with encoder-decoder architecture and temporal GRU transitions.- 🧮 **DELTA**: Policy reasoning and utility-driven intervention module for dinoprostone titration.- 🧊 Multimodal data support: Time-series, tabular, and image modalities.- 📈 Evaluation across multiple labor datasets (Dinoprostone, Multimodal Labor, Attention-Weighted).- 🧪 Ablation studies on core modules. ## 📁 Folder Structure