Time-to-event regression using partially monotonic neural networks

• URL: http://arxiv.org/abs/2103.14755v1
• Date: Fri, 26 Mar 2021 22:34:57 GMT
• Title: Time-to-event regression using partially monotonic neural networks
• Authors: David Rindt and Robert Hu and David Steinsaltz and Dino Sejdinovic
• Abstract summary: We propose SuMo-net, that uses partially monotonic neural networks to learn a time-to-event distribution. The method does not make assumptions about the true survival distribution and avoids computationally expensive integration of the hazard function.
• Score: 9.224121801193935
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a novel method, termed SuMo-net, that uses partially monotonic neural networks to learn a time-to-event distribution from a sample of covariates and right-censored times. SuMo-net models the survival function and the density jointly, and optimizes the likelihood for right-censored data instead of the often used partial likelihood. The method does not make assumptions about the true survival distribution and avoids computationally expensive integration of the hazard function. We evaluate the performance of the method on a range of datasets and find competitive performance across different metrics and improved computational time of making new predictions.

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