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Mar, 2021
适当评分规则和单调神经网络的生存回归
Time-to-event regression using partially monotonic neural networks
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David Rindt, Robert Hu, David Steinsaltz, Dino Sejdinovic
TL;DR
提出了一种基于神经网络的生存回归模型SurvivalMonotonic-net (SuMo-net)，该方法使用时间相关权重的单调限制直接优化对数似然函数，达到了多个数据集上最先进的对数似然得分，是现有神经方法的20-100×计算速度加速，适用于具有数百万观测值的数据集。
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 f
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