CERVINet-DELTA
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# CERVINet-DELTA **A Temporal Multimodal Framework for Personalized Labor Induction Forecasting and Decision Support** --- ## 📌 Overview CERVINet-DELTA is a neural probabilistic framework designed to forecast labor induction outcomes and provide real-time clinical decision support using multimodal patient data. This includes structured maternal-fetal covariates, time-series monitoring, and ultrasound imagery. The model integrates three key components: - **CERVINet**: Captures latent temporal dynamics and individualized cervical progression.- **Multivariate Outcome Decoder**: Predicts clinically meaningful targets such as delivery time, delivery mode, and neonatal condition.- **DELTA Module**: Provides utility-aware, policy-optimized treatment recommendations and risk alerts. --- ## 🧠 Key Features - 🔄 **Temporal Modeling**: Latent stochastic trajectories modeled via continuous-time recurrent units and SDEs.- 🎯 **Multitask Prediction**: Estimates time-to-active labor, delivery mode, and Apgar scores jointly.- 🧮 **Bayesian Inference**: Provides calibrated uncertainties over all predictions.- 📊 **Policy Optimization**: Learns interpretable utility-based adjustments to induction protocols.- 🧩 **Ablation Analysis**: Isolates the contribution of each module on predictive performance. --- ## 🗃️ Dataset Format Organize your dataset folder like this:
