---
title: "非交叉深度分位数回归：面向分布级生存预测的新框架"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.16864v1"
permalink: "https://feed.astralor.com/items/itm_16f5ae6c2cb49015defaf1f4"
published: "2026-08-17T17:46:41.000Z"
score: 78
channel: "ai"
tags: ["CNQ框架","Kolmogorov-Arnold Networks","Transformer","分位数回归","生存分析"]
---

# 非交叉深度分位数回归：面向分布级生存预测的新框架

## AI 点评

> 这篇工作把深度分位数回归的排序一致性问题与生存分析的时变协变量效应结合起来，值得医疗AI预后建模团队重点研读。相比只给风险比或生存曲线，它输出的个体化分位数里程碑在临床沟通和决策中更有实际意义，也能揭示被单一HR掩盖的动态风险模式。

## 速览

本研究提出右删失生存数据的非交叉分位数（CNQ）框架，联合估计多个条件生存分位数，用Kolmogorov-Arnold与Transformer骨干保证排序一致性，并给出有限样本超额风险界。27种模拟和6个队列中，CNQ在非对称分布下损失更低、区间覆盖更贴近名义水平；METABRIC与FLCHAIN案例发现被单一风险比掩盖的时变效应。

## 关键点

- CNQ框架联合估计多个条件生存分位数，并通过结构设计天然保证非交叉排序。
- 采用Kolmogorov-Arnold与Transformer骨干，给出跨所有分位水平的有限样本超额风险界。
- 27种模拟与6个队列中，非对称分布下CNQ的分位数损失优于分位数、风险与树基线，区间覆盖全部更接近名义水平。
- METABRIC与FLCHAIN临床案例显示，CNQ可恢复被单一风险比掩盖的分布级协变量效应。

## 全文

In survival analysis the way covariates act on the risk of an event often differs between early and late failure times, yet hazard- and mean-based summaries collapse this variation into a single number. Quantile-based modeling instead describes the full conditional distribution on the original time scale, but existing censored-data methods are either inflexible or produce logically inconsistent crossing quantile curves. We propose a Censored Non-crossing Quantile (CNQ) framework for right-censored data that jointly estimates several conditional survival quantiles and guarantees valid ordering by construction, with flexibility supplied by Kolmogorov-Arnold and Transformer backbones, and we establish a finite-sample excess-risk bound holding jointly across all fitted quantile levels. Across 27 simulation settings and six cohorts the framework attains lower pinball loss than quantile-, hazard- and tree-based competitors whenever the conditional distribution is asymmetric, with interval coverage closer to nominal on all six. In two clinical case studies (METABRIC, breast cancer; FLCHAIN, population mortality) it recovers covariate effects that vary across the survival distribution and would be hidden by a single hazard ratio, and yields coherent individualized quantile milestones. Code: https://github.com/BIG-S2/deepcnq

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_16f5ae6c2cb49015defaf1f4