Support Vector Machines for Censored Data

用于审查数据的支持向量机

• 批准号: 1407732
• 负责人: Michael Kosorok
• 金额: $ 41万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1407732&HistoricalAwards=false
• 关键词: Support; Vector; Machines; Censored; Data

Recent advances in medical research, including those related to the study of the human genome, have led to the development of personalized medicine. Personalized medicine describes medical treatment that is tailored to a patient based on the patient's genetic profile and other personal biomedical information. Developing personalized medical treatment regimens is challenging since it involves learning from high-dimensional patient data. Moreover, data from personalized-medicine clinical studies are typically subject to censoring, i.e., the data are not fully observed, due, for example, to patients dropping out during the course of a study. While statistical analysis of censored data is a well-developed area, most of the existing statistical tools were developed under restrictive assumptions that often do not hold for high-dimensional data settings. It is therefore important to develop an approach for analysis of censored data that can be applied to today's high-dimensional data sets. In this project we will develop novel machine learning techniques that can handle both high-dimensional and censored data. The algorithms that we will develop will be applicable not only in the field of personalized medicine, but also in other disciplines in which high-dimensional censored data are common, such as engineering, economics, and sociology.In this research, we will extend the framework of support vector machines (SVMs) to censored data. First, we will develop support vector learning techniques for different types of censored data, including right censored data, interval censoring, current status data, and multistage decision problems with censored data. We will then study the theoretical properties of these estimators, including their finite-sample properties and their asymptotic behavior. For this goal we will develop novel methodology, including new finite-sample tools for censored data. Finally, we will apply the tools that we develop to real-world data. We will compare the proposed learning methods with existing methods using theoretical tools, simulation, and analysis of real-world data. Finally, we will develop software for each of the different algorithms that we study. This software will be developed such that it can be integrated into existing machine learning software.

医学研究的最新进展，包括与人类基因组研究有关的进展，导致了个性化医疗的发展。个性化医疗描述了基于患者的遗传特征和其他个人生物医学信息为患者量身定制的医疗。开发个性化医疗方案具有挑战性，因为它涉及从高维患者数据中学习。此外，来自个性化医学临床研究的数据通常会受到审查，即，例如，由于患者在研究过程中中途退出，数据没有得到充分观察。虽然删失数据的统计分析是一个发展良好的领域，但大多数现有的统计工具都是在限制性假设下开发的，这些假设通常不适用于高维数据设置。因此，重要的是要开发一种方法来分析删失数据，可以应用到今天的高维数据集。在这个项目中，我们将开发新的机器学习技术，可以处理高维和删失数据。我们将开发的算法不仅适用于个性化医疗领域，而且适用于其他学科，其中高维删失数据是常见的，如工程，经济学和society.In这项研究中，我们将支持向量机（SVM）的框架扩展到删失数据。首先，我们将为不同类型的删失数据开发支持向量学习技术，包括右删失数据，区间删失，当前状态数据和删失数据的多阶段决策问题。然后我们将研究这些估计量的理论性质，包括它们的有限样本性质和渐近行为。为了实现这一目标，我们将开发新的方法，包括新的有限样本工具的删失数据。最后，我们将把我们开发的工具应用于现实世界的数据。我们将使用理论工具、模拟和对真实世界数据的分析来比较所提出的学习方法与现有方法。最后，我们将为我们研究的每种不同算法开发软件。该软件的开发将使其能够集成到现有的机器学习软件中。