Paper accepted to ICDM 2025
Abstract
Our paper, Interpretable and Interactive Deep Survival Analysis with Time-dependent EXtreme Gradient Integration, has been accepted to the 2025 IEEE International Conference on Data Mining (ICDM 2025).
Our paper, Interpretable and Interactive Deep Survival Analysis with Time-dependent EXtreme Gradient Integration, has been accepted to the 2025 IEEE International Conference on Data Mining (ICDM 2025). ICDM 2025 was held on November 12-15, 2025 in Washington, DC, and the paper is now available online through the IEEE Computer Society Digital Library.
Authors: Xinyu Qin, Ruiheng Yu, Armin Khayati, Zixiao Qiu, Gengyi Zou, Yan Li, and Lu Wang.
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.