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

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 r...

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Detalles Bibliográficos
Autores principales: Janssen, Alexander, Leebeek, Frank W. G., Cnossen, Marjon H., Mathôt, Ron A. A., Fijnvandraat, K., Coppens, M., Meijer, K., Schols, S. E. M., Eikenboom, H. C. J., Schutgens, R. E. G., Beckers, E. A. M., Ypma, P., Kruip, M. J. H. A., Polinder, S., Tamminga, R. Y. J., Brons, P., Fischer, K., van Galen, K. P. M., Nieuwenhuizen, L., Driessens, M. H. E., van Vliet, I., Lock, J., Hazendonk, H. C. A. M., van Moort, I., Heijdra, J. M., Goedhart, M. H. J., Al Arashi, W., Preijers, T., de Jager, N. C. B., Bukkems, L. H., Cloesmeijer, M. E., Collins, P. W., Liesner, R., Chowdary, P., Millar, C. M., Hart, D., Keeling, D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9286722/
http://dx.doi.org/10.1002/psp4.12808

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