Continuous-Signal Driven Predictive Models in Neurological Intensive Care Units

项目摘要 大多数医疗干预措施主要是为了应对临床症状或异常 在常规患者管理协议下的生理值。相反，预防性或主动性 协议可以大大改善结果，并降低不断上升的医疗保健成本，如果支持 关键临床事件的可靠和可操作的预测。然而，在大多数临床环境中， 专业人员只能访问最多到目前为止的数据。我们的长期目标 研究的目的是提供可靠的临床预测个别病人的关键参数和事件， 预测性数据挖掘的异构医疗，生理和生物数据。作为一个典型的临床 预测应用在神经重症监护环境中，拟议项目的主要目标是 证明早期识别两种常见颅内继发性损伤的有效性， 脑室扩大（脑室扩大）和颅内压（ICP）急性升高 在一个新的分类器融合框架下，将机器学习算法和新的定量 这些指标是我们小组最近在处理连续ICP信号时发现的。 预测脑室扩大和颅内压升高的需要在神经系统疾病中特别相关。 重症监护室（NICU），其中使用一系列连续监测信号来支持管理 复杂和严重神经系统疾病患者的急性期。这些重症患者是 易受多种形式的延迟但可治疗的继发性损伤。因此，及早发现 在出现临床症状之前发展二次伤害与支持采用主动治疗直接相关， 患者管理，其疗效可在随访随机临床试验中得到证实。 我们提出了两个目标，首先建立一个总体框架，支持公司持续 将生理信号转换成包括一组分类器的预测模型。这些分类器是间隔的 并且它们的结果被融合以提供改进的 预报.第二个目标包括两个子目标，以建立两个在神经重症监护中有用的原型预测 环境第一个预测是关于脑室扩大的早期发现，第二个预测是关于 预测急性ICP升高，这两种都是创伤后继发性损伤的常见形式， 出血性脑损伤 如果成功的话，ICP升高和脑室扩大的预测可能会导致潜在的范式转变 从传统的被动式患者管理到更主动的管理方案， 结果和提高效率的神经重症监护。

Project Summary Most medical interventions are primarily prescribed to respond to clinical symptoms or abnormal physiological values under conventional patient management protocols. In contrast, prophylactic or proactive protocols can substantially improve outcomes and decrease escalating costs of healthcare if supported by reliable and actionable forecasts of critical clinical events. However, in most clinical environments, health care professionals are only able to access data that are, at best, up to the present time. Long-term goal of our research is to provide reliable clinical forecasts of individual patient's critical parameters and events by predictive data mining of heterogeneous medical, physiological, and biological data. As a prototypical clinical forecast application in a neurological intensive care environment, the main objective of the proposed project is to demonstrate the efficacy of earlier recognition of two common intracranial secondary insults including acute ventriculomegaly (enlargement of the brain ventricles) and acute elevation of intracranial pressure (ICP) based on integrating, under a novel classifier fusion framework, machine learning algorithms and novel quantitative metrics that were recently discovered by our group in processing continuous ICP signals. The need for forecasting ventriculomegaly and elevated ICP is particularly relevant in a neurological intensive care unit (NICU) where an array of continuously monitored signals are used to support management of patients of complex and severe neurological disorders at their acute phase. These critically ill patients are susceptible to many forms of delayed but treatable secondary injuries. Therefore, an early detection of developing secondary insults prior to clinical symptoms is directly relevant to support the adoption of proactive patient management whose efficacy can be demonstrated in a follow-up randomized clinical trial. We propose two aims to first build a general framework supporting the incorporation continuous physiological signals into a predictive model that comprise of a set of classifiers. These classifiers are spaced at different time intervals relative to the time of interest and their results are fused to provide an improved forecast. The second aim includes two sub aims to build two protypical forecasts useful in a neurocritical care environment. The first forecast concerns early detection of ventriculomegaly and the second forecast concerns prediction of acute ICP elevation, both of which are common forms of secondary insult after traumatic and hemorrhagic brain injury. If successful, the forecast of ICP elevation and ventriculomegaly can lead to a potential paradigam shift from conventional reactive patient management to a more proactive management protocol and improve patient outcome and ehance the efficiency in a neurocritical care.