Deep compartment models: A deep learning approach for the reliable prediction of time-series data in pharmacokinetic modeling.

ABSTRACT: Nonlinear mixed effect (NLME) models are the gold standard for the analysis of patient response following drug exposure. However, these types of models are complex and time-consuming to develop. There is great interest in the adoption of machine-learning methods, but most implementations cannot be reliably extrapolated to treatment strategies outside of the training data. In order to solve this problem, we propose the deep compartment model (DCM), a combination of neural networks and ordinary differential equations. Using simulated datasets of different sizes, we show that our model remains accurate when training on small data sets. Furthermore, using a real-world data set of patients with hemophilia A receiving factor VIII concentrate while undergoing surgery, we show that our model more

SUBMITTER: Janssen A 

PROVIDER: S-EPMC9286722 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature