Variational learning of individual survival distributions

Zidi Xiu, Chenyang Tao, Duke University, Ricardo Henao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations


The abundance of modern health data provides many opportunities for the use of machine learning techniques to build better statistical models to improve clinical decision making. Predicting time-to-event distributions, also known as survival analysis, plays a key role in many clinical applications. We introduce a variational time-to-event prediction model, named Variational Survival Inference (VSI), which builds upon recent advances in distribution learning techniques and deep neural networks. VSI addresses the challenges of non-parametric distribution estimation by ($i$) relaxing the restrictive modeling assumptions made in classical models, and ($ii$) efficiently handling the censored observations, i.e. events that occur outside the observation window, all within the variational framework. To validate the effectiveness of our approach, an extensive set of experiments on both synthetic and real-world datasets is carried out, showing improved performance relative to competing solutions.
Original languageEnglish (US)
Title of host publicationACM CHIL 2020 - Proceedings of the 2020 ACM Conference on Health, Inference, and Learning
PublisherAssociation for Computing Machinery,
Number of pages9
ISBN (Print)9781450370462
StatePublished - Feb 4 2020
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-02-15


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