Abstract
Neural networks have been investigated as models for survival data using a training criterion similar to that of the Cox proportional hazards model, a criterion not designed for clinical prediction. In this paper, we develop a new survival learning algorithm where a neural network ensemble minimizes the integrated Brier score. We compare the results obtained with this method to a standard implementation of random survival forests in R and to an ensemble of linear units.
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de Montigny, S., Broët, P. (2020). Adapting Ensemble Neural Networks to Clinical Prediction in High-Dimensional Settings. In: Goutte, C., Zhu, X. (eds) Advances in Artificial Intelligence. Canadian AI 2020. Lecture Notes in Computer Science(), vol 12109. Springer, Cham. https://doi.org/10.1007/978-3-030-47358-7_15
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DOI: https://doi.org/10.1007/978-3-030-47358-7_15
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