Interpretable and Interactive Deep Survival Analysis with Time-dependent EXtreme Gradient Integration
Abstract
This paper presents an interpretable and interactive deep survival analysis framework with time-dependent extreme gradient integration.
X. Qin, R. Yu, A. Khayati, Z. Qiu, G. Zou, Y. Li, and L. Wang. Interpretable and Interactive Deep Survival Analysis with Time-dependent EXtreme Gradient Integration. In Proceedings of the 2025 IEEE International Conference on Data Mining (ICDM 2025), pp. 673-682, 2025.
Xinyu Qin
PhD Student of Biomedical Engineering
My research interests include Causal Inference, Survival Analysis, and Digital Twin.
Ruiheng Yu
PhD Student of Biomedical Engineering
My research interests include Computer vision, explainable AI, LLM in health area.
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.