Low‐dimensional neural ordinary differential equations accounting for inter‐individual variability implemented in Monolix and NONMEM

Abstract Neural ordinary differential equations (NODEs) are an emerging machine learning (ML) method to model pharmacometric (PMX) data. Combining mechanism‐based components to describe “known parts” and neural networks to learn “unknown parts” is a promising ML‐based PMX approach. In this work, the...

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Bibliographic Details
Main Authors: Dominic Stefan Bräm, Bernhard Steiert, Marc Pfister, Britta Steffens, Gilbert Koch
Format: Article
Language:English
Published: Wiley 2025-01-01
Series:CPT: Pharmacometrics & Systems Pharmacology
Online Access:https://doi.org/10.1002/psp4.13265
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