Sepsis online: learning while doing to understand biology and treatment

项目摘要/摘要 每年有100多万美国人因脓毒症住院，几乎每 五个人都不会活下来。减少败血症死亡的大多数努力都是从这样一个前提开始的 患者大体相似，且以太移动治疗较早或靶向 单一机制的治疗将改善结果。在之前的工作中，由 NIGMS R35奖，我们使用一套机器学习得出了脓毒症内型 方法在大型综合医疗系统中建立电子健康档案(EHR)。 这些内型在生物学、结果和治疗反应方面有所不同，并且是 在数以千计的患者身上重现。但它们将如何带来精准医疗呢？在这 更新，我们将利用我们的临床转化实验室和残余血液 收集以更好地了解脓毒症内型的生物学并探索新的 与病原体、微生物组和分子机制相关的结构域。我们将使用 贝叶斯因果网络和强化学习优化治疗策略 1000多万次电子病历中的内型。最后，我们将把学习转移到网上 并在护理点将内型嵌入EHR内。这些步骤将采取 脓毒症内型的科学和为临床决策提供信息的时间压力和 不确定性。通过在“活的边缘”测试内型治疗政策，我们将加强 因果推理、机械洞察和边做边学。我的计划将是 由具有机器学习、炎症、 免疫学、计算和系统生物学、因果方法、人工智能、 和健康信息技术。这项工作将进一步发展我的临床翻译能力 对初级科学家进行实验室和交叉领域的指导。

PROJECT SUMMARY / ABSTRACT More than 1 million Americans are hospitalized with sepsis each year, and nearly one in five don’t survive. Most efforts to reduce sepsis deaths begin with the premise that patients are largely similar, and that ether moving treatment earlier or targeting therapeutics to a single mechanism will improve outcomes. In prior work funded by a NIGMS R35 award, we derived sepsis endotypes using a suite of machine learning methods inside the electronic health records (EHR) in a large integrated health system. These endotypes differed in biology, outcomes, and treatment response, and were reproduced in thousands of patients. But how will they lead to precision care? In this Renewal, we will leverage our clinical translational laboratory and remnant blood collection to better understand the biology of sepsis endotypes and explore new domains related to pathogen, microbiome, and molecular mechanisms. We will use Bayesian causal networks and reinforcement learning to optimize treatment policies over endotypes in more than 10 million EHR encounters. Finally, we will move learning online and embed endotypes inside the EHR at the point-of-care. These steps will take the science of sepsis endotypes and inform clinical decisions made under time pressure and uncertainty. By testing endotype treatment policies at the “live-edge”, we will strengthen causal inference, mechanistic insight, and learn while doing. My program will be supervised by external advisory boards with expertise in machine learning, inflammation, immunology, computational and systems biology, causal methods, artificial intelligence, and health information technology. This work will further develop my clinical-translational laboratory and cross-cutting mentorship of junior scientists.