BIGDATA: Causal Inference in Large-Scale Time Series

项目总结 医院生成的大数据集可能会对医学知识和患者产生革命性影响 关心。然而，目前数据量更有可能让临床医生不堪重负，而数据的挑战可能 压倒机器学习算法。重症监护病房(ICU)以秒的分辨率生成数据， 在病人逗留的整个过程中。我们的长期目标是将这些数据转化为可操作的知识，如风险 疾病因素、早期干预目标和支持临床决策的实时信息。这是 一个广泛的问题，但在ICU尤其重要，这涉及到在 时间压力下的复杂环境。我们特别关注理解成年人的意识， 以及新生儿的神经学状况。而7%的ICU入院是由于意识丧失和程度 意识是评估预后的关键，做出艰难的选择，如何时撤回护理，以及 提供早期干预以提高生活质量，没有客观或自动的评估 意识(成人)或神经状态(新生儿)。我们已经证明，大脑反应迟钝的患者 与无反应的患者相比，激活后重新获得服从命令的能力的可能性是前者的两倍 如果没有这样的激活，然而这些评估对于常规的临床使用来说太耗时了。不过，我们也 表明在ICU中常规收集的生理数据可以作为意识分类的替代指标。它是 仍然不知道它为什么会改变，我们必须确保我们发现的模式实际上是因果的，以避免治疗 症状，而不是疾病，或启动不成功的临床试验。有两个关键障碍 阻止对意识的因果理解。首先，为每个ICU患者测量的变量不同，并且 在患者的入院过程中可能会有所不同。这导致在尝试执行以下操作时产生混淆 推断因果模型，并阻止学习适用于所有患者的单一模型，这限制了推广。 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，因为现实世界的数据缺乏基本事实，而且通常无法为隐私而共享 理由。为了应对这些挑战，我们的目标是1)开发学习可概括的因果模型的方法 潜在变量(通过在患者之间智能地共享和组合信息)，2)开发数据驱动 在保护隐私的情况下测试机器学习算法的模拟方法，以及3)应用这些方法 方法收集新生儿和神经科ICU资料。我们的目标是创造更好的意识指标和 揭示ICU中神经状态的原因及其与长期功能结果的联系。我们的工作 将医疗数据的潜在弱点(不同个体测量的不同变量)转化为优势， 并将使大规模观察性生物医学数据更好地用于实时治疗决策。

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.