Institutional Agent Based Models for Healthcare Applications (Simstitution)

我的研究计划的目标是进一步发展基于代理的建模（ABM）技术结合数据勘探/挖掘/融合。另一个目标是验证ABM与真实的数据-作为一个引人注目的方法与医疗保健建模作为主要的应用领域。数据挖掘的许多想法是新颖的，因为由于跟踪技术和在线信息的存在以及将这些来源作为ABM的输入的认可，数据变得更加容易获得。ABM需要在提供数据挖掘机会的技术方面有很强的背景，了解ABM及其好处，以及统计推断。我帮助开发了社区级流行病建模的ABM，以及用于评估ED患者访问政策和医院感染和流感样疾病建模的机构级模型。该应用程序继续开发ABM，供机构使用。从长远来看，我正在积极进行研究，以增加ABM与不同的经验数据。正在开发一个原型系统，使用智能手机作为邻近定位系统，以提供真实的数据，以改善艾德操作的代理为基础的平台。在第二种情况下，我们正在与急诊科信息系统的数据被用来作为经验输入我们的社区和机构层面的代理为基础的模型。在第三个例子中，我将使用GPU来提高我们正在开发的ABM的性能。这套建模工具正在我的实验室开发中，目前有四名研究生和一名暑期学生参与。迄今所作的努力表明，培训人力资源合格人员的机会很多。通过强调数据，这项研究保留了一个特殊的亲和力，计算机工程，无论是在实用程序和潜力，计算机工程，使这一研究领域的独特和重大贡献。虽然所概述的方法非常适合计算机工程，但所选择的医疗保健建模应用领域也具有特别重要的意义，因为这些都是未解决的问题，具有重大的公共卫生和卫生政策利益。

The objective of my research program is to further develop agent-based modeling (ABM) techniques in combination with data prospecting/mining/fusion. A further objective is to validate ABMs with real data - as a compelling methodology with healthcare modeling as the main application area. Many of the ideas for data mining are novel, in that data is becoming more readily available as a consequence of the presence of tracking technologies and on-line information as well as the recognition of these sources as input into ABMs. ABMs require a strong background in technologies that offer data mining opportunities, understanding of ABM and its benefits, and of statistical inference. I have helped develop ABMs for community-level epidemic modeling, as well as institution-level models for assessing patient access policy in EDs and modeling of nosocomial infection and influenza-like-illness. This application continues development of ABMs for use within an institution. Over the longer term, I am actively pursuing research to augment ABM with diverse empirical data. A prototype system is being developed, using Smartphones as proximity location systems to provide real data to an agent-based platform for improving ED operations. In a second instance, we are working with Emergency Department Information System data to be used as empirical inputs to both our community- and institution-level agent based models. In a third instance I will be using GPUs to improve the performance of the ABMs we are developing. This suite of modeling tools is under development in my lab, and currently engages four graduate students and one summer student. Efforts to date have demonstrated that the opportunities for the training of HQP are significant. By placing an emphasis on data, this research retains a particular affinity to computer engineering, both in utility and in the potential for computer engineering to make unique and significant contributions to this research area. While the outlined approach is highly suited to computer engineering, the chosen application area of healthcare modeling is also of particular significance as these are unsolved problems with substantial public health and health policy benefits.