Interactive & Explainable Survival Analysis
Time-to-event prediction with human-in-the-loop explanations
Interactive & Explainable Survival Analysis PlatformOverview
Interactive and Explainable Survival Analysis Algorithms provide time-to-event prediction with human-in-the-loop explanations, enabling trustworthy and interpretable decision support systems.
Key Features
- Interactive Explanation: Human-in-the-loop approach for model interpretation
- Deep Learning Integration: Advanced neural network architectures for survival analysis
- Trustworthy Predictions: Transparent and explainable outcomes
Applications
- IoT: Equipment failure prediction
- Analytics: Customer churn prediction
- Cybersecurity: Risk forecasting
- Healthcare: Patient outcome modeling
Deliverables
- DHAI Lab Survival Analysis Platform
- Survival Analysis GitHub Repository
Publications
Related publications can be found on our publications page.
Lu Wang
Assistant Professor of Biomedical Engineering
My research interests are developing and applying Machine Learning, Data Mining and Statistical methods (e.g., Multi-task Learning, Survival Analysis, Clustering, Risk Factor Analysis and Causal Discovery) on various data including gene expression, electronic health/medical records, and DNA sequencing reads for both cognitive disorders (e.g., delirium, Alzheimer’s disease, dementia, major depressive disorder) and chronic diseases (e.g., cancer, obesity, hypertension). Inspired by the human factors approach, she also designs and develops Human-Centered Artificial Intelligence tools for users to integrate, visualize, analyze, and interpret health data in order to improve the interoperability and accessibility of AI-assisted healthcare decision support.