Predicting with Confidence from Survival Data
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Proceedings of the Eighth Symposium on Conformal and Probabilistic Prediction and Applications, PMLR 105:123141, 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 socalled 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 noevent,
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 inhouse datasets from a truck manufacturing company.
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