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.

项目摘要 慢性阻塞性肺疾病（COPD）是一种异质性肺部疾病，其特征是 随后呼吸困难和令人担忧的生活质量，肺部功能的逐渐丧失。这 异质性使得很难预测健康下降并开发出针对性的治疗方法，以获得更好的患者护理。 迄今为止，研究人员试图使用标准的机器学习方法来识别更多 COPD有意义的亚型，但是这些方法通常对数据进行一般性的假设，并限制 他们在某些数据集中渗透更复杂模式的能力。那是有意义的重新分类 可能导致更具针对性的疗法和干预措施的COPD亚型难以捉摸。申请人 通过发现发现COPD亚型问题的新方式来查看COPD亚型问题 个体与疾病轨迹的关联 - 即，基于个人的相似性对个人进行分组 对环境和/或疾病引起变量的反应。机器学习方法拟议的构建 在贝叶斯非参数方面的最新进展中，这是一系列理论思想和技术的集合 允许非常灵活的数据表示。在这项职业发展建议中，适用的假设是 这些机器学习方法及其扩展 - 以及以前不利用的数据源 对于COPD亚型 - 将产生更有意义的患者子组，从而使得更好 了解该疾病的遗传和生物学基础，并最终改善患者 管理。本应用的目标1涉及评估CT评估的肺质量的实用性 - 潜在的 与常规使用的措施相比，肺气肿的歧视性测量更大 - 用于定义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。计算出的小说 将探讨基于层析成像的肌肉浪费（缓存）和肺血管修剪的措施 确定它们在亚型测定中的效率。此外，我们将扩展和测试轨迹算法 为了建模离散输出（例如物理诊断的合并症），计数数据（例如， 恶化）和事件时间数据（死亡）。在AIM 3中，申请人将扩展我们的轨迹集群 直接合并遗传和OMIC数据以进行亚型发现的算法。一起研究 在该奖项的目的中提出的建议将充分利用通过 Copdgene研究。 通过与M.D. Ron Kikinis博士的积极合作，可以执行该提案中的目标 在医学图像分析领域著名领导者，国际认可的Ed Silverman博士 COPD遗传流行病学的专家。