Sepsis online: learning while doing to understand biology and treatment

脓毒症在线：边做边学，了解生物学和治疗

• 批准号: 10406975
• 负责人: Christopher Warren Seymour
• 金额: $ 47.28万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-02 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10406975
• 关键词: American; Artificial Intelligence; Award; Biological; Biological Markers; Biology; Blood; Cessation of life; Clinical; Collection; Computational Biology; E-learning; Early treatment; Electronic Health Record; Ethers; Funding; Immunology; Inflammation; Integrated Health Care Systems; Laboratories; Lead; Learning; Machine Learning; Mentorship; Methods; Molecular; National Institute of General Medical Sciences; Outcome; Patients; Policies; Psychological reinforcement; Science; Scientist; Sepsis; Supervision; Systems Biology; Testing; Time; Uncertainty; Work; health information technology; improved; improved outcome; insight; machine learning method; microbiome; pathogen; personalized care; point of care; pressure; programs; targeted treatment; treatment optimization; treatment response

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.

项目摘要 /摘要 每年有超过100万美国人住院，几乎 五个无法生存。减少败血症死亡的大多数努力始于前提 患者在很大程度上相似，并且较早的乙醚移动治疗或靶向 单一机制的治疗将改善预后。在先前的工作中 Nigms R35奖，我们使用一套机器学习衍生出败血症 大型综合卫生系统中电子健康记录（EHR）内部的方法。 这些内型在生物学，结果和治疗反应上都不同，并且是 在数千名患者中复制。但是，它们将如何导致精确护理呢？在这个 续约，我们将利用我们的临床翻译实验室和残留的血液 收集以更好地了解败血症的生物学并探索新的 与病原体，微生物组和分子机制有关的域。我们将使用 贝叶斯因果网络和加强学习以优化治疗政策 超过1000万EHR遇到的内型。最后，我们将在线学习 并在护理点的EHR内嵌入内型。这些步骤将采取 败血症的科学和在时间压力下做出的临床决策的信息 不确定。通过测试“实时边缘”的内型治疗政策，我们将加强 因果推断，机械洞察力和学习时学习。我的程序将是 由外部咨询委员会监督，具有机器学习，炎症专业知识， 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 初级科学家的实验室和横切心态。