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.

项目概要/摘要 每年有超过 100 万美国人因脓毒症住院，其中近四分之一 大多数减少败血症死亡的努力都是从以下前提开始的： 患者在很大程度上相似，并且乙醚移动治疗更早或针对 单一机制的治疗将改善结果。 NIGMS R35 奖，我们使用一套机器学习得出脓毒症内型 大型综合卫生系统中电子健康记录 (EHR) 内的方法。 这些内型在生物学、结果和治疗反应方面有所不同，并且 但它们将如何实现精准护理呢？ 续约，我们将利用我们的临床转化实验室和剩余血液 收集以更好地了解脓毒症内型的生物学并探索新的 我们将使用与病原体、微生物组和分子机制相关的领域。 贝叶斯因果网络和强化学习可优化治疗政策 最后，我们将把学习转移到网上。 并将内型嵌入到护理点的 EHR 中。 脓毒症内型科学并为在时间压力下做出的临床决策提供信息 通过在“实时边缘”测试内型治疗政策，我们将加强不确定性。 因果推理，机械洞察，边做边学。 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、 这项工作将进一步发展我的临床转化。 实验室和对初级科学家的跨领域指导。