Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors
Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors
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发表时间：
2011-12
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通讯作者：
Chun-Nam Yu;R. Greiner;Hsiu-Chin Lin;V. Baracos
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作者：
Chun-Nam Yu;R. Greiner;Hsiu-Chin Lin;V. Baracos
患者生存时间的准确模型可以帮助癌症患者的治疗和护理。仅根据癌症部位和阶段的人群平均值提供生存时间估计的常见做法忽略了患者之间许多重要的个体差异。在本文中，我们提出了一种局部回归方法，用于基于患者属性（如血液检查和临床评估）学习患者特定的生存时间分布。当对2000多名癌症患者进行测试时，我们的方法给出的生存时间预测比流行的生存分析模型（如考克斯和Aalen回归模型）准确得多。我们的研究结果还表明，与仅使用癌症部位和阶段相比，使用患者特定属性可以将生存时间的预测误差减少多达20%。
An accurate model of patient survival time can help in the treatment and care of cancer patients. The common practice of providing survival time estimates based only on population averages for the site and stage of cancer ignores many important individual differences among patients. In this paper, we propose a local regression method for learning patient-specific survival time distribution based on patient attributes such as blood tests and clinical assessments. When tested on a cohort of more than 2000 cancer patients, our method gives survival time predictions that are much more accurate than popular survival analysis models such as the Cox and Aalen regression models. Our results also show that using patient-specific attributes can reduce the prediction error on survival time by as much as 20% when compared to using cancer site and stage only.