Boosting Transfer Learning with Survival Data from Heterogeneous Domains

Alexis Bellot, Mihaela van der Schaar
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:57-65, 2019.

Abstract

Survival models derived from health care data are an important support to inform critical screening and therapeutic decisions. Most models however, do not generalize to populations outside the marginal and conditional distribution assumptions for which they were derived. This presents a significant barrier to the deployment of machine learning techniques into wider clinical practice as most medical studies are data scarce, especially for the analysis of time-to-event outcomes. In this work we propose a survival prediction model that is able to improve predictions on a small data domain of interest - such as a local hospital - by leveraging related data from other domains - such as data from other hospitals. We construct an ensemble of weak survival predictors which iteratively adapt the marginal distributions of the source and target data such that similar source patients contribute to the fit and ultimately improve predictions on target patients of interest. This represents the first boosting-based transfer learning algorithm in the survival analysis literature. We demonstrate the performance and utility of our algorithm on synthetic and real healthcare data collected at various locations.

Cite this Paper

BibTeX
@InProceedings{pmlr-v89-bellot19a, title = {Boosting Transfer Learning with Survival Data from Heterogeneous Domains}, author = {Bellot, Alexis and van der Schaar, Mihaela}, booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics}, pages = {57--65}, year = {2019}, editor = {Chaudhuri, Kamalika and Sugiyama, Masashi}, volume = {89}, series = {Proceedings of Machine Learning Research}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v89/bellot19a/bellot19a.pdf}, url = {https://proceedings.mlr.press/v89/bellot19a.html}, abstract = {Survival models derived from health care data are an important support to inform critical screening and therapeutic decisions. Most models however, do not generalize to populations outside the marginal and conditional distribution assumptions for which they were derived. This presents a significant barrier to the deployment of machine learning techniques into wider clinical practice as most medical studies are data scarce, especially for the analysis of time-to-event outcomes. In this work we propose a survival prediction model that is able to improve predictions on a small data domain of interest - such as a local hospital - by leveraging related data from other domains - such as data from other hospitals. We construct an ensemble of weak survival predictors which iteratively adapt the marginal distributions of the source and target data such that similar source patients contribute to the fit and ultimately improve predictions on target patients of interest. This represents the first boosting-based transfer learning algorithm in the survival analysis literature. We demonstrate the performance and utility of our algorithm on synthetic and real healthcare data collected at various locations.} }
Endnote
%0 Conference Paper %T Boosting Transfer Learning with Survival Data from Heterogeneous Domains %A Alexis Bellot %A Mihaela van der Schaar %B Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Masashi Sugiyama %F pmlr-v89-bellot19a %I PMLR %P 57--65 %U https://proceedings.mlr.press/v89/bellot19a.html %V 89 %X Survival models derived from health care data are an important support to inform critical screening and therapeutic decisions. Most models however, do not generalize to populations outside the marginal and conditional distribution assumptions for which they were derived. This presents a significant barrier to the deployment of machine learning techniques into wider clinical practice as most medical studies are data scarce, especially for the analysis of time-to-event outcomes. In this work we propose a survival prediction model that is able to improve predictions on a small data domain of interest - such as a local hospital - by leveraging related data from other domains - such as data from other hospitals. We construct an ensemble of weak survival predictors which iteratively adapt the marginal distributions of the source and target data such that similar source patients contribute to the fit and ultimately improve predictions on target patients of interest. This represents the first boosting-based transfer learning algorithm in the survival analysis literature. We demonstrate the performance and utility of our algorithm on synthetic and real healthcare data collected at various locations.
APA
Bellot, A. & van der Schaar, M.. (2019). Boosting Transfer Learning with Survival Data from Heterogeneous Domains. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 89:57-65 Available from https://proceedings.mlr.press/v89/bellot19a.html.

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