BIGDATA: Causal Inference in Large-Scale Time Series

项目摘要 医院生成的大型数据集可能对医疗知识和患者产生变革性影响 在乎然而，目前的数据量更有可能压倒临床医生和数据的挑战， 压倒机器学习算法。重症监护室（ICU）以秒的分辨率生成数据， 在病人住院期间都是如此我们的长期目标是将这些数据转化为可操作的知识，就像风险一样。 疾病因素、早期干预目标和支持临床决策的实时信息。这是 这是一个广泛的问题，但在ICU中尤其重要，因为它涉及到在一个 时间压力下的复杂环境。我们特别关注理解成年人的意识， 和新生儿的神经系统状态。虽然7%的ICU入院是由于意识丧失， 意识对于评估预后至关重要，做出困难的选择，如何时撤销护理， 提供早期干预以改善生活质量，没有客观或自动评估， 意识（成人）或神经系统状态（新生儿）。我们已经证明大脑反应迟钝的病人 与无反应的患者相比， 如果没有这种激活，这些评估对于常规的临床使用来说太耗时。然而，我们也 显示，在ICU中常规收集的生理数据可以用作对意识进行分类的代理。是 仍然不知道为什么它会改变，我们必须确保我们发现的模式实际上是因果关系，以避免治疗 症状而不是疾病或启动不成功的临床试验。有两个关键的障碍 阻碍了对意识的因果理解首先，每个ICU患者测量的变量不同， 在病人入院的过程中会有所不同。这会导致在尝试 推断因果模型，并且无法为所有患者学习单一模型，从而限制了可推广性。 其次，虽然医学数据的挑战需要新的方法，但研究人员很少能够严格地 评估和比较它们，因为真实世界的数据缺乏基本事实，并且通常不能出于隐私考虑而共享 原因为了应对这些挑战，我们的目标是：1）开发学习可推广因果模型的方法， 潜在变量（通过智能地共享和组合患者之间的信息），2）开发数据驱动 在保护隐私的同时测试机器学习算法的模拟方法，以及3）应用这些方法 方法新生儿和神经ICU数据。我们的目标是创造更好的意识指标， 揭示ICU中神经系统状态的原因及其与长期功能结局的联系。我们的工作 将医疗数据的潜在弱点（个体之间测量的不同变量）转化为优势， 并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。

Project summary Large datasets generated by hospitals could have a transformative effect on medical knowledge and patient care. Yet currently the volume of data is more likely to overwhelm clinicians and the challenges of the data can overwhelm machine learning algorithms. Intensive care units (ICUs) generate data at a resolution of seconds, for the entirety of a patient's stay. Our long-term goal is to turn these data into actionable knowledge, like risk factors for a disease, early intervention targets, and real-time information to support clinical decisions. This is a broad problem, but particularly important in ICUs, which involve high stakes decisions being made in a complex environment under time pressure. We focus in particular on understanding consciousness in adults, and neurologic status in neonates. While 7% of ICU admissions are due to loss of consciousness, and degree of consciousness is critical to evaluating prognosis, making difficult choices such as when to withdraw care, and providing early interventions to improve quality of life, there are no objective or automated assessments for consciousness (adults) or neurologic status (neonates). We have shown that unresponsive patients with brain activation were twice as likely to regain the ability to follow commands compared to unresponsive patients without such activation, yet these assessments are too time consuming for regular clinical use. However we also showed that physiological data routinely collected in ICUs can be used as a proxy to classify consciousness. It is still not known why it changes and we must be sure that the patterns we find are in fact causal to avoid treating symptoms instead of a disease or launching unsuccessful clinical trials. There have been two key barriers preventing a causal understanding of consciousness. First, variables measured for each ICU patient differ, and can differ within a patient over the course of their admission. This leads to confounding when attempting to infer causal models, and has prevented learning a single model for all patients, which limits generalizability. Second, while the challenges of medical data require new methods, researchers are rarely able to rigorously evaluate and compare them, since real-world data lacks ground truth and often cannot be shared for privacy reasons. To address these challenges, we aim 1) to develop methods that learn generalizable causal models with latent variables (by intelligently sharing and combining information across patients), 2) to develop data driven simulations methods for testing machine learning algorithms while preserving privacy, and 3) to apply these methods to neonatal and neurological ICU data. We aim to create better indicators for consciousness and to uncover causes of both neurological status in ICU and its link to long-term functional outcomes. Our work turns potential weaknesses of medical data (different variables measured across individuals) into a strength, and will enable better use of large-scale observational biomedical data for real-time treatment decisions.