BIGDATA: Causal Inference in Large-Scale Time Series

大数据：大规模时间序列中的因果推断

• 批准号: 9097149
• 负责人: SAMANTHA KLEINBERG
• 金额: $ 38.46万
• 依托单位: STEVENS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2020-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9097149
• 关键词: Academic Medical Centers; Acute; Address; Admission activity; Adverse effects; Affect; Big Data; Circadian Rhythms; Clinical Trials; Complex; Conscious; Data; Decision Making; Detection; Disease; Documentation; Early Intervention; Electroencephalography; Epilepsy; Etiology; Event; Health; Hospitals; Intensive Care; Intensive Care Units; Intervention; Knowledge; Lead; Learning; Life; Medicine; Methods; Modeling; Nature; Neurologic; Noise; Outcome; Patients; Pattern; Pharmaceutical Preparations; Physiologic Monitoring; Physiological; Physiology; Property; Recovery; Sampling; Seizures; Series; Signal Transduction; Stream; Stroke; Subarachnoid Hemorrhage; Symptoms; System; Time; Trust; Uncertainty; Unconscious State; Work; base; data to knowledge; functional disability; health data; improved; individual patient; insight; mortality; multimodality; sensor; sound; stroke recovery; symptom treatment

 DESCRIPTION (provided by applicant): Data collected from intensive care units could be used to guide decision-making in real-time, but instead have often led to overwhelmed clinicians trying to uncover the signal buried in the noise. This data deluge is particularly challenging when the data are delayed, as this can lead to events being incorrectly seen as simultaneous or even out of order. Patients' health also changes at different time scales such as due to a new medication or circadian rhythms. Thus as doctors attempt to integrate the many signals to understand a patient's status, their health is a moving target. To transform the data into actionable knowledge, it is also not enough to find correlations. We must be sure that the patterns we find are truly causal to avoid treating symptoms instead of a disease or launching unsuccessful clinical trials. Our prior work, though, has found that ICU data streams can in fact be used to gain insight into recovery from stroke. In particular, we revealed that nonconvulsive seizures may be related to poor outcomes in patients with subarachnoid hemorrhage (SAH). Unlike epileptic seizures, which have a sudden onset, these seizures begin gradually, making them difficult to detect automatically. Further, many SAH patients in our study were unconscious on admission, and it is difficult to frequently and reliably assess consciousness. Therefore while progress has been made, two key barriers to using ICU data to guide treatment are a) a lack of methods for finding gradual changes in a patient's state (which could be used to alert clinicians) and b) finding causal relationships with uncertain data (where the cause may be documented as happening after the effect). To address these challenges, our specific aims are 1) to develop methods for finding timing uncertainty for each variable and using this in causal inference, 2) to develop real-time methods for finding when things change, and 3) to apply these to find when stroke patients have seizures or changes in consciousness, so these can be quickly identified and treated. We propose that by learning the reasons for errors in data, and by developing methods that specifically model their uncertain and changing nature, we will enable better use of large-scale observational biomedical data for real-time treatment decisions.

 描述（由申请人提供）：从重症监护室收集的数据可用于实时指导决策，但往往导致不堪重负的临床医生试图发现隐藏在噪音中的信号。这种数据泛滥尤其具有挑战性， 数据被延迟，因为这可能导致事件被错误地视为同时的或者甚至是无序的。患者的健康状况也会在不同的时间尺度上发生变化，例如由于新药或昼夜节律。因此，当医生试图整合许多信号来了解病人的状况时，他们的健康是一个移动的目标。要将数据转化为可操作的知识，找到相关性也是不够的。我们必须确保我们发现的模式是真正的因果关系，以避免治疗症状而不是疾病或启动不成功的临床试验。然而，我们之前的工作已经发现，ICU数据流实际上可以用来深入了解中风的恢复情况。特别是，我们发现非惊厥性癫痫发作可能与蛛网膜下腔出血（SAH）患者的不良结局有关。与突然发作的癫痫发作不同，这些发作开始是逐渐的，使得它们难以自动检测。此外，在我们的研究中，许多SAH患者在入院时无意识，很难经常和可靠地评估意识。因此，虽然已经取得了进展，但使用ICU数据指导治疗的两个关键障碍是：a）缺乏发现患者状态逐渐变化的方法（可用于提醒临床医生）和B）发现与不确定数据的因果关系（其中原因可能被记录为发生在效应之后）。为了应对这些挑战，我们的具体目标是：1）开发发现每个变量的时间不确定性的方法，并将其用于因果推理，2）开发实时方法来发现事物何时发生变化，3）将这些方法应用于发现中风患者何时癫痫发作或意识变化，以便快速识别和治疗。我们建议，通过学习数据错误的原因，并通过开发专门建模其不确定性和不断变化的性质的方法，我们将能够更好地利用大规模的观察性生物医学数据进行实时治疗决策。