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: 9282329
• 负责人: SAMANTHA KLEINBERG
• 金额: $ 37.21万
• 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/9282329
• 关键词: Academic Medical Centers; Acute; Address; Admission activity; Adverse effects; Affect; Big Data; Circadian Rhythms; Clinic; Clinical Trials; Complex; Conscious; Data; Decision Making; Detection; Disease; Documentation; Early Intervention; Electroencephalography; Epilepsy; Etiology; Event; Finding by Cause; Health; Hospitals; Individual; Intensive Care; Intensive Care Units; Intervention; Knowledge; Lead; Learning; Life; Medicine; Methods; Modeling; Nature; Neurologic; Noise; Outcome; Patient Care; Patients; Pattern; Pharmaceutical Preparations; Physiologic Monitoring; Physiological; Physiology; Property; Recovery; Rogaine; 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; 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（由适用提供）：从重症监护室收集的数据可用于实时指导决策，但经常导致临床医生试图发现噪声中内置的信号。当此数据洪水尤其具有挑战性 数据被延迟，因为这可能导致事件被错误地看作是简单甚至偶数的。患者的健康在不同的时间尺度上也发生了变化，例如由于新的药物或昼夜节律造成的。当医生试图整合许多信号以了解患者的状态时，他们的健康是一个移动的目标。为了将数据转换为可行的知识，还不足以找到相关性。我们必须确保我们发现的模式确实是灾难性的，以避免治疗症状而不是疾病或发起失败的临床试验。不过，我们先前的工作发现，ICU数据流实际上可以用于洞悉中风的恢复。特别是，我们透露，非脱驱性癫痫发作可能与蛛网膜下腔出血（SAH）患者的结局差有关。与突然发作的癫痫发作不同，这些癫痫发作逐渐开始，使它们难以自动检测。此外，我们研究中的许多SAH患者对入院而无意识，而且很难经常和可靠的评估意识。因此，尽管取得了进展，但使用ICU数据指导治疗的两个关键障碍是a）缺乏在患者状态中找到成绩变化的方法（可以用来提醒临床医生），b）找到具有不确定数据的因果关系（在效果后可能记录原因的原因）。为了应对这些挑战，我们的具体目的是1）开发方法来寻找每个变量的时机不确定性，并在催化推理中使用它，2）开发实时方法以查找何时发生变化，3）将这些方法应用于中风患者何时具有癫痫发作或意识的变化，因此可以快速识别和处理这些方法。我们建议，通过学习数据错误的原因，以及开发专门模拟其不确定和变化自然的方法，我们将更好地利用大规模的观察性生物医学数据来实时治疗决策。