- Abstract:
-
To understand trends in individual responses to medication, one can take a purely data-driven machine learning approach, or alternatively apply pharmacokinetics combined with mixed-effects statistical modelling. To take advantage of the predictive power of machine learning and the explanatory power of pharmacokinetics, we propose a latent variable mixture model for learning clusters of pharmacokinetic models demonstrated on a clinical data set investigating 11β-hydroxysteroid dehydrogenase en...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Publisher's Version
- Publisher:
- Elsevier Publisher's website
- Journal:
- Journal of Theoretical Biology Journal website
- Volume:
- 455
- Pages:
- 222-231
- Publication date:
- 2018-07-23
- Acceptance date:
- 2018-07-21
- DOI:
- EISSN:
-
1095-8541
- ISSN:
-
0022-5193
- Pubs id:
-
pubs:891514
- URN:
-
uri:152682be-23a6-4f0d-95b9-0c8fbcfc335e
- UUID:
-
uuid:152682be-23a6-4f0d-95b9-0c8fbcfc335e
- Local pid:
- pubs:891514
- Copyright holder:
- Bunte et al.
- Copyright date:
- 2018
- Notes:
- © 2018 The Authors. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/)
Journal article
Learning pharmacokinetic models for in vivo glucocorticoid activation
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