Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Pharmacometric covariate modeling using symbolic regression networks

Wahlquist, Ylva LU ; Soltesz, Kristian LU orcid and Morin, Martin LU (2022) The 6th IEEE Conference on Control Technology and Applications p.1099-1104
Abstract
A central challenge within pharmacometrics is to establish a relation between pharmacological model parameters, such as compartment volumes and diffusion rate constants, and known population covariates, such as age and body mass. There is rich literature dedicated to the learning of functional mappings from the covariates to the model parameters, once a search class of functions has been determined. However, the state-of-the-art selection of the search class itself is ad hoc. We demonstrate how neural network-based symbolic regression can be used to simultaneously find the function form and its parameters. The method is put in relation to the literature on symbolic regression and equation learning. A conceptual demonstration is provided... (More)
A central challenge within pharmacometrics is to establish a relation between pharmacological model parameters, such as compartment volumes and diffusion rate constants, and known population covariates, such as age and body mass. There is rich literature dedicated to the learning of functional mappings from the covariates to the model parameters, once a search class of functions has been determined. However, the state-of-the-art selection of the search class itself is ad hoc. We demonstrate how neural network-based symbolic regression can be used to simultaneously find the function form and its parameters. The method is put in relation to the literature on symbolic regression and equation learning. A conceptual demonstration is provided through examples, as is a road map to full-scale employment to pharmacological data sets, relevant to closed-loop anesthesia. (Less)
Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
6th IEEE Conference on Control Technology and Applications
pages
1099 - 1104
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
The 6th IEEE Conference on Control Technology and Applications
conference location
Trieste, Italy
conference dates
2022-08-23 - 2022-08-25
external identifiers
  • scopus:85144597352
ISBN
978-166547338-5
DOI
10.1109/CCTA49430.2022.9966112
project
Learning pharmacometric model structures from data
language
English
LU publication?
yes
id
af8160ef-631e-4538-b92b-e7be7e0b9414
date added to LUP
2022-06-15 11:59:14
date last changed
2023-12-04 10:29:36
@inproceedings{af8160ef-631e-4538-b92b-e7be7e0b9414,
  abstract     = {{A central challenge within pharmacometrics is to establish a relation between pharmacological model parameters, such as compartment volumes and diffusion rate constants, and known population covariates, such as age and body mass. There is rich literature dedicated to the learning of functional mappings from the covariates to the model parameters, once a search class of functions has been determined. However, the state-of-the-art selection of the search class itself is ad hoc. We demonstrate how neural network-based symbolic regression can be used to simultaneously find the function form and its parameters. The method is put in relation to the literature on symbolic regression and equation learning. A conceptual demonstration is provided through examples, as is a road map to full-scale employment to pharmacological data sets, relevant to closed-loop anesthesia.}},
  author       = {{Wahlquist, Ylva and Soltesz, Kristian and Morin, Martin}},
  booktitle    = {{6th IEEE Conference on Control Technology and Applications}},
  isbn         = {{978-166547338-5}},
  language     = {{eng}},
  pages        = {{1099--1104}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Pharmacometric covariate modeling using symbolic regression networks}},
  url          = {{https://lup.lub.lu.se/search/files/119944791/pharmacometric_covariate_modeling.pdf}},
  doi          = {{10.1109/CCTA49430.2022.9966112}},
  year         = {{2022}},
}