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

大数据时代催生了前所未有的数据规模和复杂性。然而，由于传统的数据处理和分析方法经常失败或不合适，充分利用大数据资源来获取知识和发现是一项开放的挑战。该项目开发了一种创新的方法，利用大数据来改进情绪障碍的分类，目的是改善精神疾病患者的诊断和结果。由于大数据的难以捉摸的性质，对于精神障碍来说，大数据问题天生就更加严重。精神病学界已经认识到，迫切需要一种更精确、更循证的方法来诊断和治疗疾病。事实上，最近由国家精神卫生研究所(NIMH)资助的研究发现，精神干预在出现急性发作的患者中有效的比例不到25%。考虑到精神障碍的流行，这种低有效率尤其成问题。在美国，五分之一的成年人会在一生中的某个时候经历情绪障碍(例如抑郁)。这个项目的动机是这样一个假设，即通过开发新的聚类方法，从这些大型人口数据集中识别具有临床意义的结构，可以获得更精确和个性化的精神健康疾病分类。然而，这种方法必须克服问题的复杂性和大规模真实世界电子健康数据的性质带来的大量方法学挑战。这些挑战包括复杂和未知的结构、高维、异质性、变量的复杂混合、数据缺失和稀疏性。该奖项支持由一个拥有跨学科和互补技能的团队开展的合作研究项目的启动，以开发大数据方法，以解决此类生物医学数据集成中固有的挑战。该团队的集体专业知识涵盖生物医学信息学、生物统计学、计算机和信息科学、电气和计算机工程、数学和精神病学等领域。在一个灵活和完全集成的框架中开发了一种新的方法，可以扩展到其他生物医学数据和疾病。在这个框架内，捕捉数据中相关性的不同方面的聚类方法以一种严格的方式集成在一起，不仅考虑到了模型的不确定性，而且还导致了交互式可视化，对于非专家来说，这种可视化是可以访问的，具有很强的模型解释力。具体地说，该方法将依赖于对泛化误差的Bootstrap估计器的新修改，以便在从基于拓扑的方法和机器学习方法推断的集群集合上凝聚共识。该框架还支持基于用户输入(通过可视化)对共识解决方案进行迭代改进，以纳入领域专业知识。严格识别不同人群中的个体亚群将有助于对情绪障碍进行准确和有针对性的诊断，并为个性化的循证干预提供机会。应用重点是从双相情感障碍研究网络(BDRN)收集的数据中对患有情绪障碍(双相情感障碍和严重抑郁)的个人进行分类。尽管有这样的重点，但这种方法可以推广到其他面临类似诊断和治疗挑战的疾病。事实上，该项目支持将这些方法推广到更复杂和更少精选的数据的长期愿景的第一步，例如电子健康记录、社交媒体和其他来源。该奖项由国家卫生研究院大数据向知识转化(BD2K)倡议与国家科学基金会数学科学部合作支持。

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.