Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches

Simon Olofsson, Marc Deisenroth, Ruth Misener
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:3908-3917, 2018.

Abstract

Healthcare companies must submit pharmaceutical drugs or medical device to regulatory bodies before marketing new technology. Regulatory bodies frequently require transparent and interpretable computational modelling to justify a new healthcare technology, but researchers may have several competing models for a biological system and too little data to discriminate between the models. In design of experiments for model discrimination, where the goal is to design maximally informative physical experiments in order to discriminate between rival predictive models, research has focused either on analytical approaches, which cannot manage all functions, or on data-driven approaches, which may have computational difficulties or lack interpretable marginal predictive distributions. We develop a methodology for introducing Gaussian process surrogates in lieu of the original mechanistic models. This allows us to extend existing design and model discrimination methods developed for analytical models to cases of non-analytical models.

Cite this Paper

BibTeX
@InProceedings{pmlr-v80-olofsson18a, title = {Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches}, author = {Olofsson, Simon and Deisenroth, Marc and Misener, Ruth}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, pages = {3908--3917}, year = {2018}, editor = {Dy, Jennifer and Krause, Andreas}, volume = {80}, series = {Proceedings of Machine Learning Research}, month = {10--15 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v80/olofsson18a/olofsson18a.pdf}, url = {https://proceedings.mlr.press/v80/olofsson18a.html}, abstract = {Healthcare companies must submit pharmaceutical drugs or medical device to regulatory bodies before marketing new technology. Regulatory bodies frequently require transparent and interpretable computational modelling to justify a new healthcare technology, but researchers may have several competing models for a biological system and too little data to discriminate between the models. In design of experiments for model discrimination, where the goal is to design maximally informative physical experiments in order to discriminate between rival predictive models, research has focused either on analytical approaches, which cannot manage all functions, or on data-driven approaches, which may have computational difficulties or lack interpretable marginal predictive distributions. We develop a methodology for introducing Gaussian process surrogates in lieu of the original mechanistic models. This allows us to extend existing design and model discrimination methods developed for analytical models to cases of non-analytical models.} }
Endnote
%0 Conference Paper %T Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches %A Simon Olofsson %A Marc Deisenroth %A Ruth Misener %B Proceedings of the 35th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2018 %E Jennifer Dy %E Andreas Krause %F pmlr-v80-olofsson18a %I PMLR %P 3908--3917 %U https://proceedings.mlr.press/v80/olofsson18a.html %V 80 %X Healthcare companies must submit pharmaceutical drugs or medical device to regulatory bodies before marketing new technology. Regulatory bodies frequently require transparent and interpretable computational modelling to justify a new healthcare technology, but researchers may have several competing models for a biological system and too little data to discriminate between the models. In design of experiments for model discrimination, where the goal is to design maximally informative physical experiments in order to discriminate between rival predictive models, research has focused either on analytical approaches, which cannot manage all functions, or on data-driven approaches, which may have computational difficulties or lack interpretable marginal predictive distributions. We develop a methodology for introducing Gaussian process surrogates in lieu of the original mechanistic models. This allows us to extend existing design and model discrimination methods developed for analytical models to cases of non-analytical models.
APA
Olofsson, S., Deisenroth, M. & Misener, R.. (2018). Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches. Proceedings of the 35th International Conference on Machine Learning, in Proceedings of Machine Learning Research 80:3908-3917 Available from https://proceedings.mlr.press/v80/olofsson18a.html.

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