Machine Learning Development for Subtyping COPD

用于 COPD 分型的机器学习开发

• 批准号: 9316700
• 负责人: James Ross
• 金额: $ 18.74万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2021-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9316700
• 关键词: Affect; Algorithms; Award; Bayesian Analysis; Biological; Biological Markers; Blood Vessels; Cachexia; Cause of Death; Cessation of life; Characteristics; Chronic Obstructive Airway Disease; Clinical; Collaborations; Collection; Comorbidity; Complex; Data; Data Reporting; Data Set; Data Sources; Descriptor; Development; Diagnosis; Disease; Disease Progression; Disease model; Disease susceptibility; Doctor of Medicine; Dyspnea; Environment; Environmental Risk Factor; Event; Failure; Functional Imaging; Genetic; Goals; Grouping; Health; Heterogeneity; Image; Image Analysis; Individual; Inflammatory Response; International; Intervention; Lead; Lung; Machine Learning; Measures; Medical Imaging; Methodology; Methods; Modeling; Muscular Atrophy; Output; Patient Care; Patients; Pattern; Physicians; Process; Publishing; Pulmonary Emphysema; Pulmonary Mass; Quality of life; Research; Research Personnel; Respiratory physiology; Scheme; Smoke; Statistical Models; Subgroup; Syndrome; Techniques; Testing; Time; X-Ray Computed Tomography; base; career development; cigarette smoking; clinical imaging; design; disorder subtype; flexibility; genetic association; genetic epidemiology; improved; learning strategy; mortality; novel; particle; peripheral blood; predictive modeling; response; targeted treatment

Project Summary Chronic obstructive pulmonary disease (COPD) is a heterogeneous lung condition characterized by progressive loss of lung function with subsequent increasing breathlessness and worsening quality of life. This heterogeneity makes it difficult to predict health decline and develop targeted treatments for better patient care. To date, researchers have attempted to use standard machine learning methodology to identify more meaningful subtypes of COPD, but these methods often make general assumptions about the data, limiting their ability to penetrate more complex patterns in some data sets. Thus, a meaningful reclassification of COPD subtypes that could lead to more targeted therapies and interventions has been elusive. The applicant introduces a new way of looking at the COPD subtyping problem by recasting it in terms of discovering associations of individuals to disease trajectories – i.e., grouping individuals based on their similarity in response to environmental and/or disease causing variables. The machine learning methods proposed build on the most recent advances in Bayesian nonparametrics, a collection of theoretical ideas and techniques that permit very flexible data representations. In this career development proposal, the applicant hypothesizes that these machine learning methods and extensions thereof – together with data sources not previously leveraged for COPD subtyping – will produce more biologically meaningful sub-groupings of patients, leading to a better understanding of the genetic and biological underpinnings of the disease and ultimately improved patient management. Aim 1 of this application involves evaluating the utility of CT-assessed lung mass – a potentially more discriminative measure of emphysema than conventionally used measures – for defining COPD subtypes using both K-means clustering and our disease trajectory algorithm. The goal of Aim 2 is to evaluate the utility of comorbidity data for defining COPD subtypes using our trajectory clustering algorithm. Novel computed tomography based measures of muscle wasting (cachexia) and pulmonary vascular pruning will be explored to determine their efficacy in subtype determination. Additionally, we will extend and test the trajectory algorithm in order to model discrete outputs (such as physician-diagnosed comorbidities), count data (e.g. exacerbations), and time-to-event data (death). In Aim 3, the applicant will extend our trajectory clustering algorithms to directly incorporate genetic and omics data for subtype discovery. Together, the research proposed in the aims of this award will take full advantage of the comprehensive data set available through the COPDGene study. Execution of the aims in this proposal will be possible through active collaboration with Dr. Ron Kikinis, M.D., a renowned leader in the field of medical image analysis, and Dr. Ed Silverman, an internationally recognized expert in the genetic epidemiology of COPD.

项目概要 慢性阻塞性肺疾病（COPD）是一种异质性肺部疾病，其特征是 肺功能逐渐丧失，随后呼吸困难加剧，生活质量恶化。这 异质性使得预测健康状况下降和开发有针对性的治疗方法以改善患者护理变得困难。 迄今为止，研究人员已尝试使用标准机器学习方法来识别更多 有意义的 COPD 亚型，但这些方法经常对数据做出一般性假设，限制了 他们能够洞察某些数据集中更复杂的模式。因此，有意义的重新分类 能够带来更有针对性的治疗和干预措施的慢性阻塞性肺病亚型一直难以捉摸。申请人 引入了一种看待 COPD 亚型问题的新方法，通过重新发现发现问题 个体与疾病轨迹的关联——即根据个体的相似性对个体进行分组 对环境和/或引起疾病的变量的反应。提出的机器学习方法构建 关于贝叶斯非参数学的最新进展，理论思想和技术的集合 允许非常灵活的数据表示。在这份职业发展提案中，申请人假设： 这些机器学习方法及其扩展——以及以前未利用过的数据源 用于 COPD 亚型分析——将产生更具生物学意义的患者亚组，从而获得更好的结果 了解疾病的遗传和生物学基础并最终改善患者的病情 管理。该应用的目标 1 涉及评估 CT 评估的肺质量的效用——潜在的 与传统使用的测量方法相比，肺气肿测量方法更具辨别力——用于定义慢性阻塞性肺病 (COPD) 亚型 使用 K 均值聚类和我们的疾病轨迹算法。目标 2 的目标是评估效用 使用我们的轨迹聚类算法定义 COPD 亚型的合并症数据。小说计算 将探索基于断层扫描的肌肉萎缩（恶病质）和肺血管修剪的措施 确定它们在亚型确定中的功效。此外，我们将扩展和测试轨迹算法 为了对离散输出（例如医生诊断的合并症）进行建模，对数据进行计数（例如 恶化）和事件发生时间数据（死亡）。在目标 3 中，申请人将扩展我们的轨迹聚类 直接整合遗传和组学数据以发现亚型的算法。一起，研究 该奖项的目标是充分利用通过 COPD基因研究。 通过与 Ron Kikinis 博士（医学博士）的积极合作，可以实现本提案中的目标。 医学图像分析领域的著名领导者，以及国际公认的 Ed Silverman 博士 COPD遗传流行病学专家。