A Modeling and Control Framework for Early Detection of Adverse Clinical States
用于早期检测不良临床状态的建模和控制框架
基本信息
- 批准号:1609038
- 负责人:
- 金额:$ 55.9万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2016
- 资助国家:美国
- 起止时间:2016-09-01 至 2020-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Critical care units are the highest mortality units in any hospital. These severely ill patients undergo multiple complex interventions at the same time, and care is so complex that they are extremely vulnerable to medical errors and adverse outcomes. Critical care patients are the most heavily instrumented patients in the hospital. Physiological signals are collected using many different types of sensors. These sensor signals reflect the underlying dynamic, integrated physiological state of the patient and are thus highly complex and inter-related. The biggest challenge faced by critical care physicians is that the amount and complexity of these data push the limits of what they can cognitively assimilate and relate to the overall physiological status of their patients. They are confronted by Big Data at every moment, yet they must interpret and act on them quickly. They lack the tools to do this. In this project, a goal is to develop and apply a novel class of algorithms, known as optimal change-point detection algorithms, to the problem of detecting when a patients state changes from one clinical condition to another. The application of sophisticated algorithms to healthcare will not only transform treatment in critical care units, but will be brought to the classroom to undergraduate students minoring in Computational Medicine.The research team plans to develop automated computational methods for processing physiological time series data from critical care patient sensors to quickly detect changes in clinical state. These methods will include; (i) improved selection of features that characterize patient state; (ii) optimal control algorithms to detect transitions of patient state based on these features. One innovation in the proposed approach is a re-formulated the state transition detection problem as an optimal change-point problem from the fields of controls-theory and Bayesian optimal sequential decision making. This in turn has enabled us to derive an optimal detector by first defining a cost function that reflects performance goals (e.g. maximize sensitivity, minimize false positives), and then developing the detection rule that minimizes this cost. The researchers have demonstrated that this approach decreases time to detection of epileptic seizure onset by 50% relative to other state of the art methods. Another innovation lies in how to compute features from physiological waveforms. The goal of proposed research is to determine if optimal-change point algorithms in conjunction with these new features applied to the analysis of multiple types of physiological time series data can support earlier detection of changes in the clinical conditions of patients in the critical care unit. A new course or course module that introduces statistical and mechanistic model estimation and simple early detection algorithms will be developed. This course will expose biomedical engineering students to experimental data, biophysical-based models, and statistical models of biological systems that have clinical relevance.
重症监护室是任何医院中死亡率最高的部门。这些重症患者同时接受多种复杂的干预措施,护理非常复杂,他们极易受到医疗差错和不良后果的影响。 重症监护患者是医院中使用器械最多的患者。使用许多不同类型的传感器收集生理信号。这些传感器信号反映了患者的潜在动态综合生理状态,因此高度复杂且相互关联。重症监护医生面临的最大挑战是,这些数据的数量和复杂性突破了他们能够认知吸收的极限,并与患者的整体生理状态相关。他们每时每刻都面临着大数据,但他们必须快速解释并采取行动。他们缺乏这样做的工具。在这个项目中,一个目标是开发和应用一类新的算法,被称为最佳变点检测算法,检测病人的状态从一个临床条件改变到另一个的问题。复杂算法在医疗保健领域的应用不仅将改变重症监护病房的治疗方式,还将被带到课堂上,供辅修计算医学的本科生使用。研究团队计划开发自动计算方法,用于处理来自重症监护患者传感器的生理时间序列数据,以快速检测临床状态的变化。这些方法将包括:(i)改进表征患者状态的特征的选择;(ii)基于这些特征检测患者状态的转变的最佳控制算法。在所提出的方法的一个创新是重新制定的状态转换检测问题作为一个最佳的变点问题,从控制理论和贝叶斯最优序贯决策领域。这反过来又使我们能够通过首先定义反映性能目标(例如,最大化灵敏度,最小化误报)的成本函数,然后开发最小化此成本的检测规则来获得最佳检测器。研究人员已经证明,相对于其他最先进的方法,这种方法将癫痫发作的检测时间缩短了50%。另一个创新在于如何从生理波形计算特征。拟议研究的目标是确定最佳变点算法结合这些新功能应用于多种类型的生理时间序列数据的分析,可以支持早期检测重症监护病房患者的临床状况的变化。将开发一个新的课程或课程模块,介绍统计和机械模型估计以及简单的早期检测算法。本课程将使生物医学工程专业的学生接触到具有临床相关性的生物系统的实验数据、基于生物医学的模型和统计模型。
项目成果
期刊论文数量(0)
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Sridevi Sarma其他文献
164. Linking Demographics, Geographics, and Social Media to Depression in College Students
164. 将人口统计学、地理学和社交媒体与大学生的抑郁症联系起来
- DOI:
10.1016/j.biopsych.2025.02.401 - 发表时间:
2025-05-01 - 期刊:
- 影响因子:9.000
- 作者:
Nisa Naik;Cailyn Chien;Sridevi Sarma - 通讯作者:
Sridevi Sarma
Sridevi Sarma的其他文献
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{{ truncateString('Sridevi Sarma', 18)}}的其他基金
RAPID: Data-Driven Models to Optimize Ventilator Therapy in ICU COVID Patients
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- 批准号:
2031195 - 财政年份:2020
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$ 55.9万 - 项目类别:
Standard Grant
EFRI-M3C: Robust Decoder-Compensator Architecture for Interactive Control of High-Speed and Loaded Movements
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1137237 - 财政年份:2011
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Standard Grant
PECASE: Modeling and Control of Neuronal Networks
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1055560 - 财政年份:2011
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0441316 - 财政年份:2005
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$ 55.9万 - 项目类别:
Standard Grant
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