Different Methodologies for Patient Stratification Using Survival Data

机译：使用生存数据进行患者分层的不同方法

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Clinical characterization of breast cancer patients related to their risk and profiles is an important part for making their correct prognostic assessments. This paper first proposes a prognostic index obtained when it is applied a flexible non-linear time-to-event model and compares it to a widely used linear survival estimator. This index underpins different stratification methodologies including informed clustering utilising the principle of learning metrics, regression trees and recursive application of the log-rank test. Missing data issue was overcome using multiple imputation, which was applied to a neural network model of survival fitted to a data set for breast cancer (n=743). It was found the three methodologies broadly agree, having however important differences.

机译：与他们的风险和特征相关的乳腺癌患者的临床特征是做出正确的预后评估的重要组成部分。本文首先提出了一种预测指标，该指标在应用灵活的非线性事件发生时间模型时获得，并将其与广泛使用的线性生存估计器进行比较。该指数支持不同的分层方法，包括利用学习指标原理的知识聚类，回归树和对数秩检验的递归应用。使用多重插补克服了丢失数据的问题，该插补被应用于与乳腺癌数据集拟合的神经网络生存模型（n = 743）。发现这三种方法大致相同，但有重要区别。

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来源
《Computational intelligence methods for bioinformatics and biostatistics》|2009年|p.276-290|共15页
会议地点 Genoa(IT);Genoa(IT)
作者
Ana S. Fernandes; Davide Bacciu; Ian H. Jarman; Terence A. Etchells; Jose M. Fonseca; Paulo J.G. Lisboa;
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作者单位

Faculdade de Ciencias e Tecnologia, Universidade Nova de Lisboa;

Dipartimento d'Informatica, Universita di Pisa;

School of Computing and Mathematical Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK;

School of Computing and Mathematical Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK;

Faculdade de Ciencias e Tecnologia, Universidade Nova de Lisboa;

School of Computing and Mathematical Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK;

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会议组织
原文格式 PDF
正文语种 eng
中图分类生物工程学（生物技术）;人工智能理论;
关键词
prognostic risk index; patient stratification;

机译：预后风险指数；患者分层;
入库时间 2022-08-26 14:04:26

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Different Methodologies for Patient Stratification Using Survival Data

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