Predicting with Confidence from Survival Data

Henrik Boström, Ulf Johansson, Anders Vesterberg
Proceedings of the Eighth Symposium on Conformal and Probabilistic Prediction and Applications, PMLR 105:123-141, 2019.

Abstract

Survival modeling concerns predicting whether or not an event will occur before or on a given point in time. In a recent study, the conformal prediction framework was applied to this task, and so-called conformal random survival forest was proposed. It was empirically shown that the error level of this model indeed is very close to the provided confidence level, and also that the error for predicting each outcome, i.e., event or no-event, can be controlled separately by employing a Mondrian approach. The addressed task concerned making predictions for time points as provided by the underlying distribution. However, if one instead is interested in making predictions with respect to some specific time point, the guarantee of the conformal prediction framework no longer holds, as one is effectively considering a sample from another distribution than from which the calibration instances have been drawn. In this study, we propose a modification of the approach for specific time points, which transforms the problem into a binary classification task, thereby allowing the error level to be controlled. The latter is demonstrated by an empirical investigation using both a collection of publicly available datasets and two in-house datasets from a truck manufacturing company.

Cite this Paper

BibTeX
@InProceedings{pmlr-v105-bostrom19a, title = {Predicting with Confidence from Survival Data}, author = {Bostr\"om, Henrik and Johansson, Ulf and Vesterberg, Anders}, booktitle = {Proceedings of the Eighth Symposium on Conformal and Probabilistic Prediction and Applications}, pages = {123--141}, year = {2019}, editor = {Gammerman, Alex and Vovk, Vladimir and Luo, Zhiyuan and Smirnov, Evgueni}, volume = {105}, series = {Proceedings of Machine Learning Research}, month = {09--11 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v105/bostrom19a/bostrom19a.pdf}, url = {https://proceedings.mlr.press/v105/bostrom19a.html}, abstract = {Survival modeling concerns predicting whether or not an event will occur before or on a given point in time. In a recent study, the conformal prediction framework was applied to this task, and so-called conformal random survival forest was proposed. It was empirically shown that the error level of this model indeed is very close to the provided confidence level, and also that the error for predicting each outcome, i.e., event or no-event, can be controlled separately by employing a Mondrian approach. The addressed task concerned making predictions for time points as provided by the underlying distribution. However, if one instead is interested in making predictions with respect to some specific time point, the guarantee of the conformal prediction framework no longer holds, as one is effectively considering a sample from another distribution than from which the calibration instances have been drawn. In this study, we propose a modification of the approach for specific time points, which transforms the problem into a binary classification task, thereby allowing the error level to be controlled. The latter is demonstrated by an empirical investigation using both a collection of publicly available datasets and two in-house datasets from a truck manufacturing company.} }
Endnote
%0 Conference Paper %T Predicting with Confidence from Survival Data %A Henrik Boström %A Ulf Johansson %A Anders Vesterberg %B Proceedings of the Eighth Symposium on Conformal and Probabilistic Prediction and Applications %C Proceedings of Machine Learning Research %D 2019 %E Alex Gammerman %E Vladimir Vovk %E Zhiyuan Luo %E Evgueni Smirnov %F pmlr-v105-bostrom19a %I PMLR %P 123--141 %U https://proceedings.mlr.press/v105/bostrom19a.html %V 105 %X Survival modeling concerns predicting whether or not an event will occur before or on a given point in time. In a recent study, the conformal prediction framework was applied to this task, and so-called conformal random survival forest was proposed. It was empirically shown that the error level of this model indeed is very close to the provided confidence level, and also that the error for predicting each outcome, i.e., event or no-event, can be controlled separately by employing a Mondrian approach. The addressed task concerned making predictions for time points as provided by the underlying distribution. However, if one instead is interested in making predictions with respect to some specific time point, the guarantee of the conformal prediction framework no longer holds, as one is effectively considering a sample from another distribution than from which the calibration instances have been drawn. In this study, we propose a modification of the approach for specific time points, which transforms the problem into a binary classification task, thereby allowing the error level to be controlled. The latter is demonstrated by an empirical investigation using both a collection of publicly available datasets and two in-house datasets from a truck manufacturing company.
APA
Boström, H., Johansson, U. & Vesterberg, A.. (2019). Predicting with Confidence from Survival Data. Proceedings of the Eighth Symposium on Conformal and Probabilistic Prediction and Applications, in Proceedings of Machine Learning Research 105:123-141 Available from https://proceedings.mlr.press/v105/bostrom19a.html.

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