Integrating Data, Models, and Reasoning in Critical Care

描述（由申请人提供）： 该生物工程研究合作伙伴关系的目标是集中来自学术界（麻省理工学院），工业界（飞利浦医疗系统）和临床医学（Beth Israel Deaconess Medical Center，BIDMC）的强大跨学科团队的资源，开发和评估先进的ICU患者监护系统，这将大大提高重症监护临床决策的效率，准确性和及时性。 现代重症监护病房采用了一系列令人印象深刻的技术先进的仪器，以提供详细的测量每个病人的病理生理状态。从长远来看，我们计划建立监测系统，不仅向人类用户报告这些测量结果，而且还形成病理生理学假设，最好地解释来自临床观察、床边监护仪、机械呼吸机和各种实验室测试和成像研究的丰富而复杂的相关数据。这样的系统应该减少日益增长的信息过载问题，并提供比今天的未集成的极限警报更准确和及时的警报。通过帮助医生将注意力集中在最重要的事件和患者状态的变化上，并通过建议对该状态的可能的生理解释，这种系统最终将允许早期检测甚至复杂的问题，并提供有用的指导治疗干预;因此，它们的使用应导致改善患者的结果。 为了实现这些长期目标，我们提出了一个循序渐进的方法。首先，我们将创建一个包含500个数据丰富的ICU病例的研究数据库，我们将对这些病例进行去识别和彻底注释，以便我们可以将其作为自己和其他研究人员的资源。其次，我们将开发一系列复杂的基于模型的推理方法和相应的软件来分析我们收集的数据，并创建从详细数据提取病理生理学假设的技术手段。第三，我们将评估我们的新开发的工具在实验室利用新的数据库的效用。最后，我们将在BIDMC ICU的临床实践中部署我们最成功的新技术，以比较其与现有监测系统的安全性和有效性。

DESCRIPTION (provided by applicant): The objective of this Bioengineering Research Partnership is to focus the resources of a powerful interdisciplinary team from academia (MIT), industry (Philips Medical Systems), and clinical medicine (Beth Israel Deaconess Medical Center, BIDMC) to develop and evaluate advanced ICU patient monitoring systems that will substantially improve the efficiency, accuracy, and timeliness of clinical decision making in intensive care. Modern intensive care units employ an impressive array of technologically sophisticated instrumentation to provide detailed measurements of the pathophysiological state of each patient. In the long term, we plan to build monitoring systems that not only report these measurements to human users but also form pathophysiological hypotheses that best explain the rich and complex volume of relevant data from clinical observations, bedside monitors, mechanical ventilators and a wide variety of laboratory tests and imaging studies. Such systems should reduce the ever-growing problem of information overload, and provide much more accurate and timely alarms than today's unintegrated limit alarms. By helping to focus the practitioner's attention on the most significant events and changes in the patient's state and by suggesting likely physiological interpretations of that state, such systems will eventually permit early detection of even complex problems and provide useful guidance on therapeutic interventions; thus their use should lead to improved patient outcomes. To achieve these long-term goals, we propose a step-wise approach. First, we will create a research database of 500 data-rich ICU cases that we will de-identify and thoroughly annotate so that we can make it available as a resource for ourselves and other researchers. Second, we will develop an array of sophisticated model-based and reasoning methods and corresponding software to analyze the data we collect and to create the technical means of abstracting from detailed data to pathophysiological hypotheses. Third, we will evaluate the utility of our newly developed tools in the laboratory utilizing the new database. Finally, we will deploy the most successful of our new techniques into clinical practice in the BIDMC ICUs to compare their safety and efficacy with existing monitoring systems.