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Critical Care Informatics

重症监护信息学

基本信息

批准号：
10251943
负责人：
Leo Anthony G Celi
金额：
$ 39.98万
依托单位：
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-08-01 至 2023-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10251943
关键词：
Academia Accident and Emergency department Address Admission activity Adoption Adult Archives Boston Caring Clinical Clinical Data Clinical Research Code Collaborations Communication Communities Computer software Country Credentialing Critical Care Critical Illness Data Data Collection Data Set Data Sources Data Store Databases Development Diagnostic radiologic examination Disease Educational workshop Electronic Health Record Funding Guidelines Healthcare Home Hospitals Image Industry Informatics Infrastructure Institution Intensive Care Intensive Care Units International Intervention Investigation Israel Joints Knowledge Laboratories Licensing Link London Medical Medical Technology Medical center Modification Operating Rooms Paris, France Patient Care Patients Performance Pharmaceutical Technology Physiological Physiology Population Process Reproducibility Research Research Project Grants Resolution Resources Retrospective Studies Standardization Technology Thoracic Radiography United States Universities Update Validation access restrictions base clinical care clinical database clinical decision support data archive data complexity data harmonization data modeling data resource data sharing data standards education resources evidence base health data improved international center online course open source prediction algorithm repository research study structured data success support tools symposium tool treatment effect unstructured data

项目摘要

Abstract Critical care units are home to some of the most sophisticated patient technology within hospitals. The result- ing data have the potential to improve our understanding of disease and to improve clinical care. Critically ill patients are an ideal population for clinical database investigations because the value of many treatments and interventions they receive remains largely unproven, and high-quality studies supporting or discouraging speciﬁc practices are relatively sparse [4]. Standardized critical care guidelines currently in use are dependent on an evidence base that is surprisingly weak considering the amount of data generated in the ICU [13]. The MIT Laboratory for Computational Physiology (LCP) developed and maintains the publicly available Medical Information Mart for Intensive Care (MIMIC), containing highly detailed data associated with 53,423 distinct adult ICU admissions at the Beth Israel Deaconess Medical Center in Boston [21]. MIMIC is now a widely used resource worldwide for clinical research studies, exploratory and validation analyses performed by pharmaceutical and medical technology companies, as well as for university, conference and online courses, tutorials and workshops. LCP recently released the open eICU Collaborative Research Database [24] in collaboration with Philips Healthcare, comprising de-identiﬁed clinical data associated with approximately 200,000 critical care admissions to over two hundred hospitals throughout the United States. We now intend to expand the success of our open-access, open-source approach to critical care research by releasing large new intra-operative, emergency department and imaging datasets. Importantly, we have made exciting progress with the global consortium our group is spearheading around the development of high resolution critical care databases. With our assistance, colleagues at Oxford, London, Paris, Sao Paulo, Madrid, and Beijing have made signiﬁcant progress in building their own versions of MIMIC and transforming them into the OMOP common data model. Multi-center research is challenging, because different institutions collect and store data in (sometimes dras- tically) different formats. The adoption and harmonization of data standards is a critical requirement in order for the data to be properly archived, integrated across institutions, and shared for reuse. This proposal seeks funding to: (a) support and expand our publicly available critical care data resources into new domains including pre-ICU care in the ED and OR, and serial chest X-ray imaging; b) develop the technical infrastructure needed to integrate data from international critical care units; and c) conduct research aimed at understanding and addressing the complexities of using multicenter and federated datasets in the development of predictive and clinical decision support tools, as well as in observational retrospective studies.

摘要重症监护病房是医院内一些最复杂的患者技术的发源地。结果是-- ING数据有可能提高我们对疾病的理解，并改善临床护理。病情危重患者是临床数据库研究的理想人群，因为许多治疗和他们接受的干预措施在很大程度上仍未得到证实，高质量的研究支持或阻止特定的ﬁc。实践相对稀少[4]。目前使用的标准化重症监护指南取决于考虑到ICU中产生的数据量，证据基础令人惊讶地薄弱[13]。麻省理工学院计算生理学实验室(LCP)开发并维护了重症监护医疗信息集市(MIMIC)，包含与53,423相关的非常详细的数据波士顿贝丝以色列女执事医疗中心独特的成人ICU入院情况[21]。模仿现在是一种全球广泛使用的资源，用于临床研究、探索性和验证性分析，由制药和医疗技术公司，以及大学、会议和在线课程，教程和研讨会。 LCP最近与飞利浦合作发布了开放的eICU协作研究数据库[24 医疗保健，包括与大约200,000例重症监护入院相关的明确ﬁ的临床数据到全美两百多家医院。我们现在打算扩大我们的成功通过发布大量新的术中紧急情况，采用开放访问、开放源码的方法进行重症监护研究部门和成像数据集。重要的是，我们与我们的全球财团取得了令人兴奋的进展该集团正在带头开发高分辨率重症监护数据库。在我们的帮助下，牛津大学、伦敦大学、巴黎大学、圣保罗大学、马德里大学和北京大学的同事们在建造ﬁCan 他们自己的版本模仿并将其转换为OMOP公共数据模型。多中心研究具有挑战性，因为不同的机构收集和存储数据(有时是DRAS- 技术上)不同的格式。采用和统一数据标准是一项关键要求，以便数据要妥善存档，跨机构集成，并共享以供重复使用。这项提案寻求资金以：(A)支持和扩大我们公开可用的危重护理数据资源，以新的领域，包括急诊室和手术室的ICU前护理，以及连续的胸部X光成像；b)开发技术整合来自国际重症监护病房的数据所需的基础设施；以及c)开展研究，旨在了解并解决在开发中使用多中心和联合数据集的复杂性预测和临床决策支持工具，以及观察性回顾研究。

项目成果

期刊论文数量（214）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

MIMIC-IV, a freely accessible electronic health record dataset.

DOI：
10.1038/s41597-022-01899-x
发表时间：
2023-01-03
期刊：
SCIENTIFIC DATA
影响因子：
9.8
作者：
Johnson, Alistair E. W.;Bulgarelli, Lucas;Shen, Lu;Gayles, Alvin;Shammout, Ayad;Horng, Steven;Pollard, Tom J.;Moody, Benjamin;Gow, Brian;Lehman, Li-wei H.;Celi, Leo A.;Mark, Roger G.
通讯作者：
Mark, Roger G.

Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care.

DOI：
10.1038/s41746-021-00388-6
发表时间：
2021-02-19
期刊：
NPJ digital medicine
影响因子：
15.2
作者：
Peine A;Hallawa A;Bickenbach J;Dartmann G;Fazlic LB;Schmeink A;Ascheid G;Thiemermann C;Schuppert A;Kindle R;Celi L;Marx G;Martin L
通讯作者：
Martin L

Sources of bias in artificial intelligence that perpetuate healthcare disparities-A global review.