Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods

DOI10.1002/bimj.202100381zbMATH Open1544.62335MaRDI QIDQ6594180

Göran Köber, Harald Binder, Lara M. C. Puhlmann, Andrea Chmitorz, Raffael Kalisch, Anita Schick

Publication date: 28 August 2024

Published in: Biometrical Journal (Search for Journal in Brave)

zbMATH Keywords

longitudinal datavariable selectionobservational datadynamic modelingdeep learning

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• Time-varying coefficient estimation in differential equation models with noisy time-varying covariates
• An introduction to hybrid dynamical systems
• Joint Models for Longitudinal and Time-to-Event Data
• Estimation for High-Dimensional Linear Mixed-Effects Models Using ℓ1-Penalization
• Penalized Generalized Estimating Equations for High-Dimensional Longitudinal Data Analysis
• On stability issues in deriving multivariable regression models
• Variable selection – A review and recommendations for the practicing statistician
• Using Differentiable Programming for Flexible Statistical Modeling
• Bayesian estimation of time-varying parameters in ordinary differential equation models with noisy time-varying covariates
• An Introduction to Variational Autoencoders
• Dynamical Variational Autoencoders: A Comprehensive Review
• Deep dynamic modeling with just two time points: Can we still allow for individual trajectories?
• Selection of variables for multivariable models: opportunities and limitations in quantifying model stability by resampling

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