QuBBD: Classification and clustering of medical time series data: the example of syncope

晕厥，或者更通俗地说，昏厥，是一种令人惊讶的常见和鲜为人知的情况。 拟定工作线的长期目标是确定晕厥的根本原因。 晕倒可能是由于一个或多个内部控制机制的故障。 目前，这些控制措施与观察到的症状之间没有明确的因果关系。 为了了解所涉及的机制，本项目将从分析临床数据开始，以确定不同类型晕厥的数量和特征。 更好的理解取决于对临床数据的分析，这里是时间序列，以及从这些数据中推断患者分类的能力。 将通过数学建模对各种情况进行测试，以确认所获得的分类的合理性以及每个已确定类别的病理学性质和来源。 将受试者识别为类别或组的成员的能力还使得可以利用关于该组的其他成员的信息用于个体诊断目的。 这里开发的方法将有助于实施这种方法，有时被称为“将队列研究带到床边”。 这种方法也将适用于其他疾病的研究，类似的临床数据正在收集，如癫痫。时间序列数据是无处不在的。 事实上，越来越多的应用程序的开发依赖于它们的分析，从股票市场和经济到天气预测。 实际问题包括信号匹配、分类、模式检测和早期预测。 感兴趣的信号通常是高维的和有噪声的，并且基本的动力学通常是未知的。 该奖项支持启动一个合作研究项目，该项目在医学时间序列数据的背景下解决了上述所有问题，更具体地说，是针对晕厥。 为此，提出了一种结合非参数统计、机器学习和应用数学的新范式。 第一个目标是设计和计算多变量时间序列中的特征，这些特征很好地适应于分类和聚类。 所提出的方法依赖于最近的进展，分散的数据变量的重要性的评估。 第二个目标是通过将机器学习概念扩展到这个新领域来促进数学模型的校准。目标是确定需要多少数据来“学习”生物数学模型并提高其预测能力。 该奖项由美国国立卫生研究院大数据到知识（BD2K）计划与国家科学基金会数学科学部合作支持。

Syncope or, more colloquially, fainting, is a surprisingly common and little understood condition. The long term goal of the proposed line of work is the identification of the root causes of syncope. Fainting can result from the failure of one or more internal control mechanisms. There are currently no clear causal links between these controls and the observed symptoms. In order to understand the involved mechanisms, this project will start by analyzing clinical data to determine the number and characterization of the different types of syncope. A better understanding hinges on the analysis of clinical data, here time series, and the ability to infer from these, patient classification. Various scenarios will be tested through mathematical modeling to confirm both the soundness of the obtained classification and the nature and source of the pathology for each identified class. The ability to identify subjects as members of a class or group also makes it possible to leverage information about the other members of that group for individual diagnosis purposes. The methodology developed here will contribute to the implementation of this approach, sometimes referred to as "bringing cohort studies to the bedside". This approach will also be applicable to the study of other diseases where similar clinical data are being collected such as epilepsy.Time series data are ubiquitous. In fact, the development of an ever increasing number of applications depends on their analysis, from stock market and economics to weather predictions. Practical issues include signal matching, classification, pattern detection and early prediction. Signals of interest are often high dimensional and noisy and the underlying dynamics are generally unknown. This award supports initiation of a collaborative research project that addresses all the above issues in the context of medical times series data and, more specifically, for syncope. To do so, a novel paradigm combining non-parametric statistics, machine learning, and applied mathematics is proposed. The first objective is to design and compute features in multivariate time series that are well adapted to classification and clustering. The proposed approach relies on recent advances in the evaluation of variable importance for scattered data. The second objective is to facilitate calibration of mathematical models by extending machine-learning concepts to this new realm. The goal is to determine how much data is necessary to "learn" biomathematics models and increasing their predictive power. 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.