Big Data and Deep Learning for the Interictal-Ictal-Injury Contiuum

发作间期-发作期-损伤连续体的大数据和深度学习

• 批准号: 10761842
• 负责人: Michael Brandon Westover
• 金额: $ 9.1万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-07 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10761842
• 关键词: Big; Data; Deep; Learning; Interictal

Project Summary/Abstract: Big Data and Deep Learning for the Interictal-Ictal-Injury Continuum Brain monitoring in critical care has grown dramatically over the past 20 years with the discovery that a large proportion of ICU patients suffer from subclinical seizures and seizure-like electrical events, collectively called “ictal-interictal-injury continuum abnormalities” (IIICAs), detectable only by electroencephalography (EEG). This growth has created a crisis in critical care: It is clear that IIICAs damage the brain and cause permanent neurologic disability. Yet detection of IIICAs by expert visual review is often delayed suggesting we need better tools for real-time monitoring, to cope with the deluge of ICU EEG data. In other cases, IIICAs appear to be harmless epiphenomena, and many worry that increased awareness of IIICAs has created an epidemic of overly-aggressive prescribing of anticonvulsant drugs leading to preventable adverse events and costs. This crisis highlights critical unmet needs for automated EEG monitoring for IIICAs, and a better understanding of which types of IIICAs cause neural injury and warrant intervention. Causes of IIICAs range widely, from primary brain injuries like hemorrhagic stroke and intracranial hemorrhage, to systemic medical illnesses like sepsis and uremia. Until recently, this massive clinical heterogeneity has been an insurmountable barrier to understanding the impact of IIICAs on neurologic outcome. However, recent advances in deep learning, coupled with the unprecedented availability of a massive dataset developed by our team over the last three years, makes it feasible for the first time to systematically study the relationship between IIICAs and neurologic outcomes. To meet the need for better monitoring tools and better models for understanding IIICAs, we will take a deep learning approach to leverage the as-yet untapped information in a massive ICU EEG dataset. We will pursue three Specific Aims: SA1: Comprehensively label all occurrences of IIICAs in a massive set of cEEG recordings, thus preparing the EEG data for training computers to detect IIICA patterns; SA2: Develop supervised DL algorithms to detect IIICAs as accurately as human experts, thus providing powerful tools for both research on IIICAs and for clinical brain monitoring; SA3: Estimate the effect of IIICAs on neurologic outcome: we will develop models to quantify effects of IIICAs on risk for disability after controlling for inciting illness and other clinical factors, and to predict effects of interventions to suppress IIICAs. This work will provide four crucial benefits to advance the field of precision critical care neurology, and by extension, our ability to provide optimal neurologic care for patients during critical illness. 1) Improved understanding of the clinical significance of seizure like IIICA states; 2) development of robust tools and algorithms for critical care brain telemetry; 3) a unique, massive, publicly available, thoroughly annotated dataset that will enable other researchers to further advance the field; and 4) a testable model that predicts which types of cEEG abnormalities warrant aggressive treatment, setting the stage for interventional trials.

项目摘要/摘要：发作间期-发作期-损伤连续体的大数据和深度学习 在过去的20年里，重症监护中的大脑监测急剧增长， ICU患者的比例患有亚临床癫痫发作和癫痫样电事件，统称为 “发作-发作间期-损伤连续异常”（IIICA），仅可通过脑电图（EEG）检测。这 增长已经在重症监护中造成了危机：很明显，IIICA会损害大脑并导致永久性的 神经系统残疾然而，通过专家目视检查检测IIICA通常会延迟，这表明我们需要 更好的实时监测工具，以科普ICU EEG数据的泛滥。在其他情况下，IIICA似乎 是无害的附带现象，许多人担心，对IIICA的认识增加已经造成了一种流行病， 过度使用抗惊厥药物，导致可预防的不良事件和成本。这 危机凸显了IIICA自动EEG监测的关键未满足需求， 其中IIICA类型导致神经损伤并需要干预。 IIICA的原因范围很广，从原发性脑损伤，如出血性中风和颅内 出血，全身性疾病，如败血症和尿毒症。直到最近，这个大规模的临床 异质性一直是理解IIICA对神经系统影响的不可逾越的障碍， 结果。然而，深度学习的最新进展，加上前所未有的可用性， 我们的团队在过去三年中开发的大量数据集，首次使其成为可能， 系统研究IIICA与神经系统结局的关系。 为了满足对更好的监测工具和更好的模型的需求，我们将采取一个 深度学习方法，以利用大规模ICU EEG数据集中尚未开发的信息。我们将 追求三个具体目标：SA 1：全面标记大量IIICA中的所有事件， cEEG记录，从而为训练计算机检测IIICA模式准备EEG数据; SA 2：开发 有监督的DL算法可以像人类专家一样准确地检测IIICA，从而提供强大的工具 用于IIICA研究和临床脑部监测; SA 3：估计IIICA对 神经学结局：我们将开发模型，以量化IIICA对残疾风险的影响， 用于诱发疾病和其他临床因素，并预测抑制IIICA的干预措施的效果。 这项工作将提供四个关键的好处，以推进精确的重症监护神经病学领域， 延伸，我们有能力为危重病患者提供最佳的神经护理。1)改进 理解癫痫样IIICA状态的临床意义; 2）开发可靠的工具， 重症监护脑遥测算法; 3）一个独特的，大规模的，公开的，彻底注释 数据集，这将使其他研究人员进一步推进该领域;和4）可测试的模型，预测 哪种类型的cEEG异常需要积极治疗，为干预性试验奠定基础。

项目成果

STAN: spatio-temporal attention network for pandemic prediction using real-world evidence.

• DOI: 10.1093/jamia/ocaa322
• 发表时间: 2021-03-18
• 期刊: Journal of the American Medical Informatics Association : JAMIA
• 作者: Gao J;Sharma R;Qian C;Glass LM;Spaeder J;Romberg J;Sun J;Xiao C
• 通讯作者: Xiao C

A machine learning approach to predict progression on active surveillance for prostate cancer.

• DOI: 10.1016/j.urolonc.2021.08.007
• 发表时间: 2022-04
• 期刊: Urologic oncology
• 作者: Nayan M;Salari K;Bozzo A;Ganglberger W;Lu G;Carvalho F;Gusev A;Schneider A;Westover BM;Feldman AS
• 通讯作者: Feldman AS

Evidence-driven spatiotemporal COVID-19 hospitalization prediction with Ising dynamics.

• DOI: 10.1038/s41467-023-38756-3
• 发表时间: 2023-05-29
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Gao, Junyi;Heintz, Joerg;Mack, Christina;Glass, Lucas;Cross, Adam;Sun, Jimeng
• 通讯作者: Sun, Jimeng

Machine learning applications for therapeutic tasks with genomics data.

• DOI: 10.1016/j.patter.2021.100328
• 发表时间: 2021-10-08
• 期刊: Patterns (New York, N.Y.)
• 作者: Huang K;Xiao C;Glass LM;Critchlow CW;Gibson G;Sun J
• 通讯作者: Sun J

Artificial intelligence foundation for therapeutic science.

• DOI: 10.1038/s41589-022-01131-2
• 发表时间: 2022-10
• 期刊: NATURE CHEMICAL BIOLOGY
• 影响因子: 14.8
• 作者: Huang, Kexin;Fu, Tianfan;Gao, Wenhao;Zhao, Yue;Roohani, Yusuf;Leskovec, Jure;Coley, Connor W.;Xiao, Cao;Sun, Jimeng;Zitnik, Marinka
• 通讯作者: Zitnik, Marinka