NEW FUNCTIONAL MODELS FOR BIOMEDICAL DATA

生物医学数据的新功能模型

• 批准号: 6342219
• 负责人: WENSHENG GUO
• 金额: $ 12.37万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-01-01 至 2003-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6342219
• 关键词: animal data; biomarker; brain neoplasms; computer assisted medical decision making; computer program /software; data collection methodology /evaluation; diabetes mellitus; diagnosis design /evaluation; ectopic pregnancy; human data; liver transplantation; mathematical model; model design /development; respiratory distress syndrome of newborn

DESCRIPTION (Adapted from the Applicant's Abstract): Functional data are common in cancer studies and other biomedical research, such as biomarkers measured over time in cancer experiments and other clinical trials, growth curves, hormone profiles, circadian rhythms in biological signals and drug activities. Although much work has been done on functional models for independent data, extensions to incorporate complex designs and correlations are still very preliminary. The first specific aim of this application is to develop general functional models using smoothing splines that can incorporate complex designs and allow flexible nonparametric between-curve random effects. Another long-existing problem for functional models is the heavy computational demand. Except in very simple cases, most of the current estimation procedures need to invert large dimensional matrices. This prevents applications to large data sets. In this application, we will develop O(N) sequential estimation procedures for general functional models by modifications of the Kalman filtering and fixed interval smoothing. Serial measurements have become a natural part of patient monitoring and medical diagnosis. In monitoring and predicting a patient-specific outcome based on laboratory tests or other biomarkers, we can obtain more accurate predictions by borrowing the strength from the existing patient population profiles over time. In medical diagnosis, we can gain efficiency by using the up-to-date cumulative information and compare the individual profile with the existing group profiles. In this application, we will develop dynamic patient monitoring and diagnostic methods, in which flexible functional models will be used to model both the population and individual profiles. With the proposed sequential estimation procedures, these methods can be efficiently calculated and implemented in a real time setting, which leads to rapid medical interventions. Most current statistical inference procedures rely on the distributional assumptions, such as the normality assumption. When the distribution is multimodal, it is often difficult to make parametric assumptions, and therefore nonparametric density estimation methods are needed. In this application, we will develop general density models and their associated inference procedures, and apply these methods to accessible biomedical data sets.

描述（改编自申请人的摘要）：功能数据很常见 在癌症研究和其他生物医学研究中，例如测量的生物标志物 随着时间的推移，在癌症实验和其他临床试验中，生长曲线， 激素概况、生物信号的昼夜节律和药物活性。 尽管在独立数据的功能模型上已经做了很多工作， 合并复杂设计和相关性的扩展仍然非常重要 初步的。该应用程序的第一个具体目标是开发通用的 使用平滑样条线的功能模型可以合并复杂的设计 并允许灵活的非参数曲线间随机效应。其他 功能模型长期存在的问题是大量的计算需求。 除了非常简单的情况外，大多数当前的估计程序都需要 对大维矩阵求逆。这阻碍了大数据的应用 套。在此应用中，我们将开发 O(N) 顺序估计 通过卡尔曼修正的通用功能模型程序 滤波和固定间隔平滑。 串行测量已成为患者监测和治疗的自然组成部分 医疗诊断。监测和预测患者特定的结果 基于实验室测试或其他生物标志物，我们可以获得更准确的 通过借鉴现有患者群体的力量进行预测 随着时间的推移。在医疗诊断中，我们可以通过使用 最新的累积信息并将个人资料与 现有的组资料。在此应用程序中，我们将开发动态患者 监测和诊断方法，其中灵活的功能模型将是 用于对总体和个人概况进行建模。随着提议的 顺序估计程序，这些方法可以有效地计算 并在实时环境中实施，从而实现快速医疗 干预措施。 当前大多数统计推断程序都依赖于分布 假设，例如正态性假设。当分布为 多模态，通常很难做出参数假设，因此 需要非参数密度估计方法。在这个应用程序中，我们 将开发通用密度模型及其相关的推理程序， 并将这些方法应用于可访问的生物医学数据集。