ArticleProceedings of machine learning research2021
Dynamic Survival Analysis with Individualized Truncated Parametric Distributions.
Article in Proceedings of machine learning research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Evaluating Dimensionality Reduction for Patient-Reported Outcome-Based Survival Modeling in Patients With Head and Neck Cancer.JCO clinical cancer informatics · 2025Article
- Life, death, and statins: association of statin prescriptions and survival in older general practice patients.Primary health care research & development · 2024Article
- Personalized prediction for multiple chronic diseases by developing the multi-task Cox learning model.PLoS computational biology · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Dynamic survival analysis is a variant of traditional survival analysis where time-to-event predictions are updated as new information arrives about an individual over time. In this paper we propose a new approach to dynamic survival analysis based on learning a global parametric distribution, followed by individualization via truncating and renormalizing that distribution at different locations over time. We combine this approach with a likelihood-based loss that includes predictions at every time step within an individual's history, rather than just including one term per individual. The combination of this loss and model results in an interpretable approach to dynamic survival, requiring less fine tuning than existing methods, while still achieving good predictive performance. We evaluate the approach on the problem of predicting hospital mortality for a dataset with over 6900 COVID-19 patients.
Indexed as
Identifiers
35372850PMC8969882What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.