Logical Analysis of Data and Cardiac Surgery Risk

数据和心脏手术风险的逻辑分析

基本信息

批准号：
7074706
负责人：
Eugene Hubert Blackstone
金额：
$ 33.62万
依托单位：
CLEVELAND CLINIC LERNER COM-CWRU
依托单位国家：
美国
项目类别：
财政年份：
2004
资助国家：
美国
起止时间：
2004-07-01 至 2008-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7074706
关键词：
cardiovascular disorder risk clinical research computer data analysis heart surgery human data human subject method development statistics /biometry

项目摘要

DESCRIPTION (provided by applicant): The Logical Analysis of Data (LAD) is an exciting new approach that can be used for systematically analyzing large databases for the purpose of developing and validating clinically useful risk prediction schemes. Unlike standard regression techniques, LAD does not primarily focus on individual risk factors and two-way interactions between them. Rather, LAD is designed to identify complex patterns of findings, or syndromes, that predict outcomes. This method has been applied to problems in economics, seismology, and oil exploration, and very recently, also to medicine. Cardiovascular risk stratification may be an appropriate application for the LAD as it relies on simultaneous and sequential collections of many different data elements. In preliminary work, we have shown that LAD works well for prediction of death among low-risk patients referred for stress electrocardiography without imaging. In this application there are three Specific Aims: 1. Apply LAD to develop and validate risk prediction instruments among patients undergoing different types of cardiac surgery. 2. Compare the predictive value of LAD predictive instruments with predictive instruments developed using standard statistical methods, including multiple time-phase parametric modeling. 3. Develop predictive instruments using relative risk forests, a new Monte Carlo method for estimating risk values in large survival data settings with large numbers of correlated variables. Relative risk forests are an adaptation of random forests introduced by Breiman. When possible these methods will be compared to LAD. Internal estimates for the generalization error, a measure of how well the method will generalize to other data settings, will be computed and will be used in the development of the predictive instrument. Relative risk forests will also be compared to several other non-deterministic methods, including boosting and spike and slab variable selection. All of these techniques can be used to develop complex models while maintaining good prediction error and are ideal for high dimensional problems where traditional methods breakdown. Although this project will focus on risk assessment among patients undergoing cardiac surgery, it is important to recognize that we are primarily interested in the value of LAD as a means of analyzing very large and complex data sets within a medical sphere. Hence, the applicability of this work goes beyond determination of risk of patients undergoing cardiac surgery.

描述（由申请人提供）：数据（LAD）的逻辑分析是一种令人兴奋的新方法，可用于系统地分析大型数据库，以开发和验证临床上有用的风险预测方案。与标准回归技术不同，LAD并不主要关注单个风险因素和之间的双向相互作用。相反，LAD旨在识别预测结果的复杂模式或综合症的复杂模式。该方法已应用于经济学，地震学和石油探索方面的问题，以及最近也用于医学。心血管风险分层可能是对LAD的适当应用，因为它依赖于许多不同数据元素的同时和顺序集合。在初步工作中，我们表明，LAD可以很好地预测未经影像成像而引用压力心电图的低风险患者死亡。在此应用中，有三个特定的目标：1。在接受不同类型的心脏手术的患者中使用LAD来开发和验证风险预测工具。 2。将LAD预测仪器的预测价值与使用标准统计方法开发的预测工具的预测价值，包括多个时间相参数建模。 3。使用相对风险森林开发预测工具，这是一种新的蒙特卡洛方法，用于估计具有大量相关变量的大生存数据设置中的风险值。相对风险森林是对布雷曼引入的随机森林的适应。如果可能，这些方法将与LAD进行比较。将计算该方法将其推广到其他数据设置的量度的衡量标准，并将用于预测仪器的开发。还将将相对风险森林与其他几种非确定性方法进行比较，包括增强和尖峰和平板变量选择。所有这些技术都可以用来开发复杂的模型，同时保持良好的预测错误，并且是传统方法分解的高维问题的理想选择。尽管该项目将集中于接受心脏手术的患者之间的风险评估，但重要的是要认识到，我们主要对LAD的价值感兴趣，作为分析医疗领域内非常大而复杂的数据集的一种手段。因此，这项工作的适用性超出了接受心脏手术患者的风险的确定。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Cost effectiveness of biventricular pacemakers in heart failure patients.

双心室起搏器在心力衰竭患者中的成本效益。

DOI：
10.2165/00129784-200606020-00007
发表时间：
2006
期刊：
American journal of cardiovascular drugs : drugs, devices, and other interventions
影响因子：
0
作者：
Heerey,Adrienne;Lauer,Michael;Alsolaiman,Firas;Czerr,Jennifer;James,Karen
通讯作者：
James,Karen

Breast cancer prognosis by combinatorial analysis of gene expression data.