Abstract
Being able to accurately predict the time to event of interest, commonly known as survival analysis, is extremely beneficial in healthcare for modeling disease progression, identifying prognostic factors, assessing risk of health by building survival models in health aging, precision medicine, supporting clinical decision making. In order to be usable by healthcare providers, survival analysis models need to be accurate, interpretable, and trustable. Efficient interaction between human stakeholders (e.g., developers, domain experts and/or end-users) and clear model interpretation not only improve the model performance but also enhance human trust. The primary goal of this paper is to develop algorithm and method that support implementation of trustworthy and time-efficient data-driven decision making for prevention and early intervention. Our experimental results on one public cancer datasets demonstrate the algorithm efficiency for predicting survival time of cancer patients.
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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.
Xinyu Qin 秦兴宇
Phd student of Biomedical Engineering
My research interests include computing and programmable matter.