Mixed effect estimation in deep compartment models: Variational methods outperform first-order approximations
Alexander Janssen,
Frank C. Bennis,
Marjon H. Cnossen
et al.
Abstract:This work focusses on extending the deep compartment model (DCM) framework to the estimation of mixed-effects. By introducing random effects, model predictions can be personalized based on drug measurements, enabling the testing of different treatment schedules on an individual basis. The performance of classical first-order (FO and FOCE) and machine learning based variational inference (VI) algorithms were compared in a simulation study. In VI, posterior distributions of the random variables are approximated … Show more
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