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: 10577884
• 负责人: SAMANTHA KLEINBERG
• 金额: $ 28.15万
• 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 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10577884
• 关键词: Ablation; Address; Admission activity; Adult; Age; Algorithms; Altered Level of Consciousness; Behavior assessment; Big Data; Brain; Brain Injuries; Cardiovascular system; Caring; Classification; Clinical; Clinical Trials; Cognitive; Complex; Conscious; Consensus; Consumption; Critical Illness; Data; Disease; Early Intervention; Electroencephalography; Environment; Evaluation; Goals; Health; Hospitals; Human; Impairment; Individual; Intelligence; Intensive Care Units; Intervention; Knowledge; Lead; Learning; Link; Machine Learning; Measurement; Measures; Medical; Methods; Modeling; Neonatal; Neonatal Intensive Care Units; Neurologic; Neurological status; Outcome; Output; Patient Care; Patients; Pattern; Performance; Physiological; Population; Prevalence; Privacy; Prognosis; Proxy; Quality of life; Research; Research Personnel; Resolution; Risk Factors; Series; Signal Transduction; System; Testing; Time; Unconscious State; Withdrawal; Withdrawing Care; Work; causal model; clinical decision support; clinical practice; effective intervention; functional outcomes; improved; individual variation; insight; large datasets; learning strategy; machine learning algorithm; neonatal patient; neonate; open source; premature; pressure; prevent; privacy preservation; respiratory; response; simulation; stroke patient; symptom treatment; tool

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.

项目摘要 医院产生的大型数据集可能会对医学知识和患者产生变革性影响 关心。然而，目前，数据量更有可能压倒临床医生，数据的挑战可以 压倒性的机器学习算法。重症监护单元（ICU）以秒的分辨率生成数据， 在整个患者住宿中。我们的长期目标是将这些数据变成可行的知识，例如风险 疾病，早期干预目标和实时信息的因素，以支持临床决策。这是 一个广泛的问题，但在ICU中尤其重要，其中涉及在 在时间压力下的复杂环境。我们特别关注的是了解成年人的意识， 和新生儿的神经系统状况。虽然有7％的ICU入院是由于意识的丧失和程度 意识对于评估预后至关重要，做出艰难的选择，例如何时退出护理以及 提供早期干预措施以改善生活质量，没有客观或自动化的评估 意识（成人）或神经系统状况（新生儿）。我们已经表明，大脑反应迟钝的患者 与无反应的患者相比，恢复遵循命令的能力的可能性是激活的两倍 没有这种激活，这些评估对于定期临床使用过于耗时。但是我们也是如此 表明在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。这是 仍然不知道为什么它发生变化，我们必须确定我们发现的模式实际上是因果 症状而不是疾病或发起不成功的临床试验。有两个关键障碍 防止因果理解意识。首先，为每个ICU患者测量的变量都不同，并且 在患者入院过程中可能会有所不同。这会导致试图混淆 推断因果模型，并阻止了所有患者学习单一模型，这限制了普遍性。 其次，尽管医疗数据的挑战需要新方法，但研究人员很少能够严格 评估和比较它们，因为现实世界的数据缺乏地面真理，并且通常无法共享隐私 原因。为了应对这些挑战，我们的目标是1）开发与以 潜在变量（通过智能共享和组合患者的信息），2）开发数据驱动 保存隐私时，用于测试机器学习算法的仿真方法，以及3）应用这些方法 新生儿和神经ICU数据的方法。我们旨在为意识和 揭示了ICU中神经系统状况的原因及其与长期功能结果的联系。我们的工作 将医学数据的潜在弱点（跨个体测量的不同变量）变成强度， 并将更好地利用大规模的观察生物医学数据来实时治疗决策。