Automated Author ProfileXiaonan, Nie
Xiaonan, Nie
Current S-Index
3.6
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
1.8
Average Dataset Index per dataset
Total Datasets
2
Total datasets for this author
Average FAIR Score
73.1%
Average FAIR Score per dataset
Total Citations
0
Total citations to the author's datasets
Total Mentions
0
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 3.6 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
CERVINet-DELTA: Multimodal Predictive Modeling for Dinoprostone-Induced Labor Outcomes> A transformer-driven, attention-guided framework for individualized labor induction outcome prediction and decision support, integrating clinical features, time series signals, and ultrasound imagery.## 🧠 OverviewCERVINet-DELTA is a multimodal machine learning system designed to predict and optimize clinical outcomes of labor induction using dinoprostone. Combining structured clinical data, dynamic uterine activity, and imaging features, it supports both high-accuracy forecasting and real-time decision-making during the labor process.This repository provides the full pipeline from data preprocessing, model architecture, training scripts, ablation studies, to figure generation and result visualization.---## 📊 Key Features- Multimodal Fusion: Bishop score components, uterine contractions, and ultrasound data- Transformer-based Attention: Cross-modal learning via dynamic attention weighting- Time-series Prediction: Accurate estimation of labor onset and delivery time- Explainability: Attention heatmaps and utility saliency for clinical interpretability- Policy Optimization Layer (DELTA): Counterfactual analysis and adaptive intervention---## 📁 Project Structure```bash.├── data/ # Raw and preprocessed datasets├── models/ # CERVINet and DELTA architecture modules├── train/ # Training scripts and configuration├── inference/ # Evaluation, prediction and figure generation├── figures/ # Visual outputs for publications├── utils/ # Helper functions (metrics, loaders, samplers)├── data.py # Dataset class and loading functions├── requirements.txt # Python dependencies└── README.md # Project documentation
Authors
- Xiaonan, Nie
0 Citations0 Mentions73% FAIR1.8 Dataset Index
10.5281/zenodo.16420169July 2025
CERVINet-DELTA: Multimodal Predictive Modeling for Dinoprostone-Induced Labor Outcomes> A transformer-driven, attention-guided framework for individualized labor induction outcome prediction and decision support, integrating clinical features, time series signals, and ultrasound imagery.## 🧠 OverviewCERVINet-DELTA is a multimodal machine learning system designed to predict and optimize clinical outcomes of labor induction using dinoprostone. Combining structured clinical data, dynamic uterine activity, and imaging features, it supports both high-accuracy forecasting and real-time decision-making during the labor process.This repository provides the full pipeline from data preprocessing, model architecture, training scripts, ablation studies, to figure generation and result visualization.---## 📊 Key Features- Multimodal Fusion: Bishop score components, uterine contractions, and ultrasound data- Transformer-based Attention: Cross-modal learning via dynamic attention weighting- Time-series Prediction: Accurate estimation of labor onset and delivery time- Explainability: Attention heatmaps and utility saliency for clinical interpretability- Policy Optimization Layer (DELTA): Counterfactual analysis and adaptive intervention---## 📁 Project Structure```bash.├── data/ # Raw and preprocessed datasets├── models/ # CERVINet and DELTA architecture modules├── train/ # Training scripts and configuration├── inference/ # Evaluation, prediction and figure generation├── figures/ # Visual outputs for publications├── utils/ # Helper functions (metrics, loaders, samplers)├── data.py # Dataset class and loading functions├── requirements.txt # Python dependencies└── README.md # Project documentation
Authors
- Xiaonan, Nie
0 Citations0 Mentions73% FAIR1.8 Dataset Index
10.5281/zenodo.16420170July 2025