Integrative Predictors of Temporomandibular Osteoarthritis

摘要 该应用程序建议开发高效的基于Web的数据管理、挖掘和分析，以 整合和分析TMJ OA患者的临床、生物和高维成像数据。基于 在我们发表的结果中，我们假设髁状突骨结构的类型、临床症状和 生物介体是无法识别的TMJ OA进展严重程度的指标。高效 以最大化数据价值的方式捕获、整理、管理、集成和分析这些数据 可获得性对于这种综合性的TMJ OA患者的科学进步和好处至关重要 信息可能会使。使用现有数据库处理高维数据库变得越来越困难 数据库管理工具或传统的处理应用程序，创造了对创新的持续需求 接近了。为此，DCBIA在大学。密歇根大学与北卡罗来纳大学合作 卡罗莱纳、德克萨斯大学MD安德森癌症中心和Kitware Inc. 我们将在分子、临床和影像水平上对TMJ OA患者的个体进行定量表征 面临更严重预后风险的表型，以及未来治疗的目标。 拟议的基于网络的系统--计算和集成数据存储(DSCI)--将远程 计算机机器学习、图像分析和来自预期收集的纵向数据的高级统计 关于TMJ骨关节炎患者的资料。由于它在网络上无处不在的设计，DSCI软件的安装将不再 是必需的。我们的长期目标是为TMJ的骨关节炎创建软件和数据存储库。是这样的 存储库需要在分布式计算环境中维护数据，以允许 来自多个临床中心的数据库，并共享用于TMJ分类的训练模型。在第四年和第五年 拟议的工作，在北方大学牙医学院传播和培训临床医生 卡罗尔大学明尼苏达州和俄勒冈州健康科学部将允许扩大拟议的研究。在目标1中， 我们将测试最先进的神经网络结构，以开发一个组合软件模块，该模块将包括 最高效、最准确的神经网络体系结构和先进的统计数据，可用于采矿成像、临床和 基线时确定的生物TMJ骨关节炎标志物。在目标2中，我们建议开发新的数据分析工具， 评估各种机器学习和统计预测模型的性能，包括定制- 高斯过程回归、极端增强树、多元变系数模型、套索、岭和 弹性网络、随机森林、pdfCluster、决策树和支持向量机。这样的自动化解决方案 将利用新兴的计算技术来确定纵向OA进展的风险指标 TMJ健康和疾病的队列。

ABSTRACT This application proposes the development of efficient web-based data management, mining, and analytics, to integrate and analyze clinical, biological, and high dimensional imaging data from TMJ OA patients. Based on our published results, we hypothesize that patterns of condylar bone structure, clinical symptoms, and biological mediators are unrecognized indicators of the severity of progression of TMJ OA. Efficiently capturing, curating, managing, integrating and analyzing this data in a manner that maximizes its value and accessibility is critical for the scientific advances and benefits that such comprehensive TMJ OA patient information may enable. High dimensional databases are increasingly difficult to process using on-hand database management tools or traditional processing applications, creating a continuing demand for innovative approaches. Toward this end, the DCBIA at the Univ. of Michigan has partnered with the University of North Carolina, the University of Texas MD Anderson Cancer Center and Kitware Inc. Through high-dimensional quantitative characterization of individuals with TMJ OA, at molecular, clinical and imaging levels, we will identify phenotypes at risk for more severe prognosis, as well as targets for future therapies. The proposed web-based system, the Data Storage for Computation and Integration (DSCI), will remotely compute machine learning, image analysis, and advanced statistics from prospectively collected longitudinal data on patients with TMJ OA. Due to its ubiquitous design in the web, DSCI software installation will no longer be required. Our long-term goal is to create software and data repository for Osteoarthritis of the TMJ. Such repository requires maintaining the data in a distributed computational environment to allow contributions to the database from multi-clinical centers and to share trained models for TMJ classification. In years 4 and 5 of the proposed work, the dissemination and training of clinicians at the Schools of Dentistry at the University of North Carol, Univ. of Minnesota and Oregon Health Sciences will allow expansion of the proposed studies. In Aim 1, we will test state-of-the-art neural network structures to develop a combined software module that will include the most efficient and accurate neural network architecture and advanced statistics to mine imaging, clinical and biological TMJ OA markers identified at baseline. In Aim 2, we propose to develop novel data analytics tools, evaluating the performance of various machine learning and statistical predictive models, including customized- Gaussian Process Regression, extreme boosted trees, Multivariate Varying Coefficient Model, Lasso, Ridge and Elastic net, Random Forest, pdfCluster, decision tree, and support vector machine. Such automated solutions will leverage emerging computing technologies to determine risk indicators for OA progression in longitudinal cohorts of TMJ health and disease.