Continuous and discrete-time survival prediction with neural networks

DOI10.1007/S10985-021-09532-6OpenAlexW3202929943MaRDI QIDQ2074086

Ørnulf Borgan, Håvard Kvamme

Publication date: 4 February 2022

Published in: Lifetime Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1910.06724

zbMATH Keywords

interpolationdiscretizationneural networkstime-to-event prediction

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Survival analysis and censored data (62Nxx)

Related Items (1)

Forecasting of COVID-19 fatality in the USA: comparison of artificial neural network-based models

Uses Software

• survival
• pycox
• dynpred
• DeepSurv

Cites Work

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• Random survival forests
• Modeling discrete time-to-event data
• Piecewise exponential models for survival data with covariates
• Survival analysis. Techniques for censored and truncated data.
• Life Tables with Concomitant Information
• On the Use of Indicator Variables for Studying the Time-Dependence of Parameters in a Response-Time Model

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