Institutional Agent Based Models for Healthcare Applications (Simstitution)

我的研究计划的目标是进一步开发基于代理的建模(ABM)技术，并将其与数据勘探/挖掘/融合相结合。另一个目标是用真实数据验证ABM--作为一种引人注目的方法，将医疗保健建模作为主要应用领域。数据挖掘的许多想法都是新颖的，因为由于跟踪技术和在线信息的存在，以及将这些来源确认为ABM的输入，数据正变得更加容易获得。ABMS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。我帮助开发了用于社区级流行病建模的ABM，以及用于评估急诊室患者准入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在机构内使用的ABM。从长远来看，我正在积极进行研究，用不同的经验数据来增强ABM。正在开发一个原型系统，使用智能手机作为近距离定位系统，向基于代理的平台提供真实数据，以改进急救行动。在第二种情况下，我们正在使用紧急情况部门信息系统数据，这些数据将用作我们基于社区和机构级代理的模型的经验输入。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中开发，目前雇用了四名研究生和一名暑期学生。迄今为止的努力表明，HQP的培训机会很大。通过将重点放在数据上，这项研究保留了与计算机工程的特别亲和力，无论是在实用性上，还是在计算机工程对这一研究领域做出独特而重大贡献的潜力方面。虽然概述的方法非常适合于计算机工程，但所选择的医疗保健建模的应用领域也具有特别重要的意义，因为这些都是具有重大公共卫生和卫生政策好处的未解决问题。 该计划的意义在于验证了基于代理的建模在医疗保健中作为基于证据的决策支持工具的有效性，医疗保健是一个实质性的公共政策和经济优先领域。

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. The significance of this program is in validating agent based modelling as evidence based decision support tools in healthcare, which is an area of substantive public policy and economic priority.