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Deep Learning and Streaming Analytics for Prediction of Adverse Events in the ICU

用于预测 ICU 不良事件的深度学习和流分析

基本信息

批准号：
9983413
负责人：
SHAMIM NEMATI
金额：
$ 19.02万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-03 至 2021-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9983413
关键词：
Address Adverse event Affect Algorithms American Antibiotics Antihypertensive Agents Beds Benchmarking Big Data Blood Pressure Caring Clinic Clinical Clinical Data Collection Communication Computerized Medical Record Computers Critical Care Critical Illness Data Data Analyses Data Set Databases Decision Trees Deterioration Differential Equation Disease Progression Early Diagnosis Ensure Environment Goals Heart Rate Hospital Mortality Hospitalization Hospitals Hour Inflammation Inflammation Mediators Inflammatory Response Intensive Care Units Intervention Intravenous Laboratories Link Liquid substance Literature Machine Learning Measurement Medical Medicare Medicine Methods Microbiology Modeling Modernization Monitor Morphologic artifacts Nature Organ Pathologic Patient Monitoring Patients Pattern Pattern Recognition Performance Pharmaceutical Preparations Physiological Reporting Reproducibility Research Research Personnel Resolution Resuscitation Risk Risk Assessment Risk stratification SCAP2 gene Sampling Sepsis Series Serum Severity of illness Stream System Techniques Technology Telemedicine Testing Therapeutic Intervention Time United States Validation Visit Work base beneficiary cohort cost data acquisition deep learning experience feeding high dimensionality improved machine learning algorithm mathematical model mortality risk multi-scale modeling multimodality novel outcome forecast parallel computer patient safety physiologic model portability prognostic public health relevance respiratory safety practice sensor septic patients simulation standard of care time use

项目摘要

DESCRIPTION (provided by applicant) Critical care medicine in the United States costs over 80 billion dollars annually. Over the past decade the rate of intensive care unit (ICU) use has been increasing, with a recent study reporting almost one in three Medicare beneficiaries experiencing an ICU visit during the last month of their lives. Every year, sepsis, a medical condition characterized by whole-body inflammation, strikes between 800,000 and 3.1 million Americans, killing approximately one in four patients affected. There is currently no definite treatment for sepsis in spite of many clinicl trials. However, early detection of sepsis and timely initiation of interventions are widely considered as important determinants of patient survival. However, basic care tasks (such as microbiological sampling and antibiotic delivery within 1 h, fluid resuscitation, and risk stratification using serum lactate or alternative), which are known to benefit most patients, are not performed in a timely manner. Previous literature suggests that high-resolution vital signs (such as heart rate, blood pressure, respiratory rate, etc.), and other sequential measurements within the electronic medical records (EMRs), can be dynamically integrated using Machine Learning techniques to help with early detection of sepsis. With the ubiquity of inexpensive high- capacity storage and high-bandwidth streaming technology it is now possible to monitor patients' vital signs continuously (for instance, the research application developed by the Emory hospital ICU uses IBM's streaming analytics platform to transmit over 100,000 real-time data points per 100 beds, per second). Despite this continuous feed of data, commonly used acuity scores, such as APACHE and SAPS, are based on snapshot values of these vital signs (typically the worst values during a 24 hours period). This limitation is partially due to unavailability of computationally efficient and robust algorithms capable of finding predictive features in multivariate, nonlinear and nonstationary sequential data, which may reveal inter- organ communication and disintegration of causal couplings with critical illnesses such as sepsis. We have recently developed a novel Machine Learning algorithm to discover automatically a collection of predictive multivariate dynamical patterns in a database of patient time-series, which can be used to classify patients or to monitor progression of disease in a given patient. The primary goal of this proposal is to apply our method to assess the predictive power of high- resolution multivariate time-series of vital signs and sequentially recorded EMR data in the ICU for early detection of sepsis and risk stratification of septic patient. To accomplish this, we aim to benchmark our technique on a large ICU cohort (the MIMIC II database with over 60,000 patients), as well as simulated data from a multiscale mathematical model of influence of inflammatory mediators on dynamical patterns of vital signs. Next, the technique will be externally validated on two separate ICU sepsis cohorts (the Emory Sepsis dataset and the Mayo Clinic Metric dataset). Finally, we will provide a real-time implementation of the proposed algorithm in an streaming environment (such as the IBM streaming analytics), in order to address the Big Data challenge of harnessing real-time, streaming sensor data from bedside monitors within the ICU, while enabling advanced pattern recognition and real-time forecasting. Ultimately we believe these methods can change the current standard of care through faster recognition and initiation of basic care, as well as guiding interventional strategis based on severity of illness and mechanisms underlying physiological deterioration.

描述（由申请人提供）美国的重症医学每年花费超过800亿美元。在过去的十年中，重症监护病房（ICU）的使用率一直在增加，最近的一项研究报告说，几乎三分之一的医疗保险受益人在生命的最后一个月内经历了ICU访问。每年，败血症，一种以全身炎症为特征的医学疾病，袭击80万至310万美国人，造成约四分之一的患者死亡。尽管有许多临床试验，但目前仍没有明确的治疗败血症的方法。然而，脓毒症的早期发现和及时开始干预被广泛认为是患者生存的重要决定因素。然而，已知对大多数患者有益的基本护理任务（如1小时内微生物采样和抗生素输送、液体复苏和使用血清乳酸盐或替代品进行风险分层）没有及时执行。先前的文献表明，高分辨率的生命体征（如心率、血压、呼吸频率等），以及电子医疗记录（EMR）内的其他顺序测量，可以使用机器学习技术动态集成，以帮助早期检测败血症。随着廉价的大容量存储和高带宽流技术的普及，现在可以连续监测患者的生命体征（例如，埃默里医院ICU开发的研究应用程序使用IBM的流分析平台每秒每100张病床传输超过100，000个实时数据点）。尽管这种数据的连续馈送，但常用的敏锐度评分（诸如APACHE和SAPS）是基于这些生命体征的快照值（通常是24小时期间的最差值）。这种限制部分是由于无法获得能够在多变量、非线性和非平稳序列数据中找到预测特征的计算有效和鲁棒的算法，这可能揭示器官间通信和与诸如败血症的危重疾病的因果耦合的解体。我们最近开发了一种新的机器学习算法，可以在患者时间序列数据库中自动发现预测性多变量动态模式的集合，这些模式可以用于对患者进行分类或监测给定患者的疾病进展。该提案的主要目标是应用我们的方法来评估ICU中生命体征的高分辨率多变量时间序列和顺序记录的EMR数据的预测能力，以用于脓毒症的早期检测和脓毒症患者的风险分层。为了实现这一目标，我们的目标是在一个大型ICU队列（超过60，000名患者的MIMIC II数据库）上对我们的技术进行基准测试，以及来自炎症介质对生命体征动态模式影响的多尺度数学模型的模拟数据。接下来，该技术将在两个单独的ICU脓毒症队列（Emory脓毒症数据集和马约诊所指标数据集）上进行外部验证。最后，我们将在流媒体环境（如IBM流媒体分析）中提供所提出算法的实时实现，以解决利用ICU内床边监护仪的实时流媒体传感器数据的大数据挑战，同时实现高级模式识别和实时预测。最终，我们相信这些方法可以通过更快地识别和启动基本护理来改变当前的护理标准，以及根据疾病的严重程度和生理恶化的机制指导干预策略。