QuBBD: Collaborative Research: Interactive Ensemble clustering for mixed data with application to mood disorders

QuBBD：协作研究：混合数据的交互式集成聚类及其在情绪障碍中的应用

• 批准号: 1557593
• 负责人: David Gotz
• 金额: $ 2.15万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1557593&HistoricalAwards=false
• 关键词: QuBBD; Collaborative; Research; Interactive; Ensemble

The Big Data era has given rise to data of unprecedented size and complexity. However, fully leveraging Big Data resources for knowledge and discovery is an open challenge due to the fact that conventional methods of data processing and analysis often fail or are inappropriate. This project develops an innovative approach that utilizes Big Data to improve the classification of mood disorders for the purpose of improving diagnosis and outcomes for psychiatric patients. Big Data issues are inherently more severe for mental disorders because of their elusive nature. The psychiatric community has recognized the critical need for a more precise, evidence-based approach for the diagnosis and treatment of disease. In fact, recent studies funded by the National Institute of Mental Health (NIMH) have found that psychiatric interventions were effective in less than 25% of patients presenting with an acute episode. This low efficacy rate is especially problematic given the prevalence of mental disorders. Mood disorders alone (e.g., depression) will be experienced by 1 in 5 adults in the United States at some point in their lives. This project is motivated by the hypothesis that a more precise and personalized classification of mental health disease can be obtained through the development of novel clustering methods that identify clinically significant structures with these large population data sets. However, such an approach must overcome a large number of methodological challenges introduced by the complexity of the problem and the nature of large-scale real-world electronic health data. These challenges include, among others, complex and unknown structure, high dimensionality, heterogeneity, complex mixtures of variables, missing data, and sparsity. This award supports initiation of a collaborative research project, carried out by a team with interdisciplinary and complimentary skill sets, to develop methods for big data that address challenges inherent in the integration of biomedical data of this type. Collective expertise of the team spans the areas of biomedical informatics, biostatistics, computer and information science, electrical and computer engineering, mathematics, and psychiatry. A novel methodology is developed in a flexible and fully integrated framework that can be extended to other biomedical data and diseases. Within this framework, clustering methods that capture different aspects of relatedness in the data are integrated in a rigorous way that not only accounts for model uncertainty, but also results in an interactive visualization that is accessible with strong model interpretability for the non-expert. Specifically, the methodology will rely on novel modifications to bootstrap estimators of generalization error for the purpose of assembling a consensus over an ensemble of clusters inferred from topology-based and machine learning approaches. The framework also supports iterative refinement of the consensus solution based on user input (via the visualization) to incorporate domain expertise. The rigorous identification of sub-groups of individuals within heterogeneous populations will facilitate accurate and targeted diagnosis for mood disorders, and provide opportunity for personalized evidence-based interventions. Applications focus on clustering individuals with mood disorders (bipolar disorder and major depression) from data collected in the Bipolar Disorder Research Network (BDRN). Despite this focus, the methodology is generalizable to other diseases that face similar challenges for diagnosis and treatment. In fact, this project supports the first steps of a long-term vision of generalizing the methods to more complex and less curated data, such as electronic health records, social media, and other sources. This award is supported by the National Institutes of Health Big Data to Knowledge (BD2K) Initiative in partnership with the National Science Foundation Division of Mathematical Sciences.

大数据时代已经引起了前所未有的规模和复杂性的数据。 但是，由于传统的数据处理和分析方法经常失败或不合适，因此完全利用大数据资源进行知识和发现是一个开放挑战。 该项目开发了一种创新的方法，该方法利用大数据来改善情绪障碍的分类，以改善精神病患者的诊断和结果。 由于其难以捉摸的性质，大数据问题对精神障碍的本质更为严重。 精神科界已经认识到对诊断和治疗疾病的更精确，基于证据的方法的迫切需要。 实际上，由美国国家心理健康研究所（NIMH）资助的最新研究发现，精神科干预措施在不到25％的患者中有效，表现出急性发作。 鉴于精神障碍的患病率，这种低功效率尤其有问题。 在美国的某个时刻，仅五分之一的成年人就会经历单独的情绪障碍（例如，抑郁症）。 该项目的激励是由假设通过开发新的聚类方法来获得精神健康疾病的更精确和个性化分类，这些方法可以通过这些大量的人群数据集鉴定出具有临床意义的结构。 但是，这种方法必须克服问题的复杂性和大规模现实世界电子健康数据的性质引入的许多方法论挑战。 这些挑战包括复杂且未知的结构，高维，异质性，变量的复杂混合物，缺失数据和稀疏性。 该奖项支持启动一个具有跨学科和免费技能的团队进行的协作研究项目，以开发大数据的方法，以解决此类生物医学数据集成中固有的挑战。 团队的集体专业知识跨越了生物医学信息学，生物统计学，计算机和信息科学，电气和计算机工程，数学和精神病学领域。 一种新的方法是在灵活且完全集成的框架中开发的，可以扩展到其他生物医学数据和疾病。 在此框架内，捕获数据中相关性不同方面的聚类方法以严格的方式集成，不仅说明了模型不确定性，而且还会导致交互式可视化，并且可以使用强大的模型可解释性来访问非专家。 具体而言，该方法将依靠新颖的修改来引导概括误差的估计值，目的是在基于拓扑和机器学习方法中推断出的集合集合，以组成共识。 该框架还支持基于用户输入（通过可视化）的共识解决方案的迭代改进，以合并域专业知识。 对异质种群中个体的子组的严格识别将促进对情绪障碍的准确诊断，并为个性化的基于证据的干预措施提供机会。 应用于躁郁症研究网络（BDRN）中收集的数据的情绪障碍（双相情感障碍和重度抑郁症）的聚类。 尽管有这种重点，但该方法仍可以推广到其他疾病，这些疾病面临诊断和治疗的类似挑战。 实际上，该项目支持将方法推广到更复杂和策划的数据（例如电子健康记录，社交媒体和其他来源）的长期愿景的第一步。 该奖项得到了美国国立卫生研究院大数据的支持（BD2K）倡议，并与国家科学基金会数学科学部合作。