TREE-STRUCTURED STATISTICAL METHODS

树结构统计方法

• 批准号: 3199845
• 负责人: RICHARD ALLEN OLSHEN
• 金额: $ 18.65万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-07-05 至 1994-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3199845
• 关键词: computer assisted diagnosis; computer data analysis; computers; digital imaging; heart disorder diagnosis; human subject; lymphoma; magnetic resonance imaging; mathematical model; model design /development; neoplasm /cancer diagnosis; statistics /biometry

Since the publication of the book Classification and Regression Trees, the program CART, which embodies tree-structured methods developed in the book, has been used widely and with success. In particular, there have been substantial applications to problems of diagnosis and prognosis in cancer and cardiology. Ideas in the book have been extended to cover censored survival data and have been applied to assessing the relationship of dose intensity and other prognostic factors in diffuse large cell lymphoma. Also, CART-like pruning of large trees to smaller ones has been incorporated into new algorithms for lossy data compression in digital radiography. The present proposal is for research on new capabilities for and further applications of tree-structured methods. The areas of research have a wide range, from achieving supercomputer speed in tree construction by using distributed computing on a workstation network to expanding tree-structured methods from data compression to enhance magnetic resonance images of lymphomatous mediastinal masses. Also, we will have access to a data set gathered from more than 12,000 subjects for purposes of comparing new techniques with old ones in the problem of quickly diagnosing myocardial infarction for. patients who enter the emergency room with chief complaint of acute chest pain. Particular improvements and new capabilities to existing algorithms include but are not limited to time series and curve recognition, multiple response regression, analysis of contingency tables, local risk estimation, s g of trees, more effective linear combination splits, spline splits, polishing trees, density estimation and clustering, distributed computing, and two-step lookahead.

自《分类与回归》一书出版以来 Trees，程序CART，它体现了开发的树结构方法 在书中，已被广泛使用并取得成功。 特别是，有 已在诊断和预后问题上得到了实质性应用 在癌症和心脏病学方面。 书中的想法已扩展到涵盖 审查生存数据并已应用于评估 弥漫性弥漫性贫血中剂量强度与其他预后因素的关系 大细胞淋巴瘤。 此外，像 CART 一样将大树修剪成小树 已被纳入有损数据压缩的新算法中 在数字放射线摄影中。 本提案旨在研究新能力 树结构方法的进一步应用。 研究领域 范围很广，从在树中实现超级计算机的速度 通过在工作站网络上使用分布式计算来构建 将树结构方法从数据压缩扩展到增强 纵隔淋巴瘤肿块的磁共振图像。 另外，我们 将有权访问从 12,000 多名受试者收集的数据集 为了比较新技术与旧技术解决问题的目的 快速诊断心肌梗塞。进入的患者 急诊室，主诉为急性胸痛。 对现有算法的特殊改进和新功能 包括但不限于时间序列和曲线识别， 多重响应回归、列联表分析、局部风险 估计，树的s g，更有效的线性组合分裂， 样条分割、抛光树、密度估计和聚类， 分布式计算和两步前瞻。