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，其特征在于 肺功能逐渐丧失，随后呼吸困难加重，生活质量恶化。这 异质性使得难以预测健康下降和开发针对性治疗以更好地护理患者。 到目前为止，研究人员试图使用标准的机器学习方法来识别更多的 有意义的COPD亚型，但这些方法往往对数据进行一般性假设， 它们能够穿透某些数据集中更复杂的模式。因此，一个有意义的重新分类， 可能导致更有针对性的治疗和干预的COPD亚型一直难以捉摸。申请人 介绍了一种新的方式来看待COPD亚型问题， 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/或致病变量的反应。提出的机器学习方法建立 关于贝叶斯非参数学的最新进展，一系列理论思想和技术， 允许非常灵活的数据表示。在这份职业发展建议书中，申请人假设， 这些机器学习方法及其扩展-连同先前未利用的数据源 对于COPD亚型-将产生更有生物学意义的患者亚组，导致更好的 了解疾病的遗传和生物学基础，并最终改善患者 管理本申请的目的1涉及评估CT评估的肺肿块的效用-一种潜在的 比常规使用的测量方法更具鉴别力的肺气肿测量方法-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亚型的共病数据。新型计算机 将探索基于断层扫描的肌肉萎缩（恶病质）和肺血管修剪的测量， 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，计数数据（例如， 急性加重）和至事件发生时间数据（死亡）。在目标3中，申请人将扩展我们的轨迹聚类 算法直接结合基因和组学数据进行亚型发现。在一起，研究 在这个奖项的目的提出将充分利用全面的数据集，通过 COPDGene研究。 通过与罗恩基基尼斯博士（医学博士）的积极合作，一 医学图像分析领域的著名领导者，以及国际公认的医学图像分析专家艾德西尔弗曼博士， COPD遗传流行病学专家。