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arXiv Machine Learning By Zhe Aurore Li, Quentin Clairon, C\'ecilia Samieri, Rodolphe Thi\'ebaut, M\'elanie Prague, C\'ecile Proust-Lima

Neural ODE enhanced linear mixed effect models for estimating complex association patterns of time-varying covariates with the marker trajectory

Read the original on arXiv Machine Learning →

The paper introduces Neural ODE-LMM, a hybrid model that integrates a Neural Ordinary Differential Equation into a linear mixed‑effects framework to flexibly learn time‑varying associations between exposures and outcomes. By encoding covariate trajectories into a continuous‑time latent state, the method preserves standard likelihood inference while capturing complex, potentially cumulative effects without pre‑specifying functional forms. Simulations demonstrate accurate recovery of instantaneous and cumulative effects, and application to the 3C cohort uncovers BMI and fasting glucose trajectories linked to cognitive decline.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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