Statistical methods for biosignals with varying domains

描述（由申请人提供）：临床护理和大型观察性研究的特点是在医院就诊期间进行高强度的健康监测，然后在两次就诊之间进行长时间的低强度或无监测。住院期间获得的数据来自许多新技术，例如非常密集采样的生物信号记录（脑电图、心电图、健康评分）和高分辨率多模态成像（MRI、CT、PET）。这类数据的一个主要特点是，它是在特定主题的一段时间内收集的。事实上，住院监测的时长和数量因受试者而异，这对于研究住院期间和出院后的健康状况都是非常有用的。其中一个例子是最近对重症监护病房（ICU）急性呼吸窘迫综合征（ARDS）患者的研究。对于每个受试者，在ICU住院期间，每天收集每个受试者的顺序器官衰竭评估（SOFA）评分，这是一种常用的评分系统，用于测量ICU中器官功能障碍。ICU的长度

DESCRIPTION (provided by applicant): Clinical care and large observational studies are characterized by periods of intense health monitoring during hospital visits followed by long periods of low-intensity or no-monitoring between visits. Data obtained during in-hospital visits come from a host of new technologies, such as very densely sampled biosignal recordings (EEG, ECG, health scores) and high resolution multi-modality imaging (MRI, CT, PET). A major characteristic of this type of data is that it is collected for a period of time that is subject-spcific. Indeed, the in-hospital length and amount of monitoring varies between subjects, and is highly informative both for studying health outcomes in the hospital and after discharge. One among many examples is a recent study of subjects admitted to the Intensive Care Unit (ICU) with Acute Respiratory Distress Syndrome (ARDS). For each subject the Sequential Organ Failure Assessment (SOFA) score, a commonly- used scoring system to measure organ dysfunction in the ICU, was collected daily for each subject for the duration of their ICU stay. The ICU length of stay is different by subject and likely to be highly informative of current and future health outcomes. In this application, a set of relevant problems are conceptualized and distilled to statistical aims to address specific complexities associated with this type of data sampling. Specifically, the proposal addresses the following fundamental unsolved problems in studies that collect high density biosignals: 1) introducing statistical models for the association between high density biosignals with uneven support and health outcomes; 2) developing functional registration-by-prediction models that transform the support of biosignals to provide best prediction of health outcomes; and 3) developing models for describing the cross-sectional and longitudinal variability of biosignals obtained in studies with rare -but intense- health monitorin. While focus lies on research studies that collect quasi- continuous ultra-high resolution biosignals for subject-specific lengths of time, methods will be generalizable to many other studies with similar data sampling structures. 2