Understanding the Mechanism of Social Network Influence in Health Outcomes throug

描述(由申请人提供)：SMASH(活动、社交和健康数据的语义挖掘)系统的设计和实现研究将解决对数据挖掘工具的迫切需求，以帮助理解医疗社交网络(如YesiWell)对持续减肥的影响，其中数据是多维的、时间的、语义上不同的和非常敏感的。系统的设计和实施将取决于五个具体目标。第一个目标是开发一种新的数据挖掘和统计学习方法，以了解使健康行为在社交网络中传播的关键因素(目标1)。我们建议为用于描述医疗数据和社会网络的语义特征的概念开发一个形式化且具有表现力的语义Web本体。然后，我们将在医疗保健和社交网络中的领域知识与这些本体概念之间的正式映射之间架起桥梁(目标2)。接下来，我们在影响力建模和预测的基础上提出了新的推荐方法。此外，我们将制定方法，利用该建议作为更好地组织社会网络的一种手段，以便在网络中采用最佳健康行为能够迅速和可持续地传播(目标3)。为了在社会网络和健康数据的数据挖掘过程中保护人类主体的隐私，我们通过隐私保护分析层来考虑差异隐私的实施。我们将开发新的解决方案来保护不同的隐私，以挖掘人类受试者的动态健康数据和社会活动(目标4)。为了支持这项研究，我们将开发一个网络可访问的门户网站，以便其他几乎没有接受过数据挖掘培训的研究人员可以共享数据挖掘工具、本体和社会网络分析结果(目标5)。在该项目结束时，数据资源、工具、本体和技术将提供给更大的研究社区。这项工作是由数据挖掘和机器学习专家之一的PI、Daniel Lowd和俄勒冈大学的卫生政策专家Jessica Greene、和平卫生实验室的YesiWell的负责人Brigitte Piniewski MD、肯特州立大学的复杂网络挖掘专家金若明、北卡罗来纳大学夏洛特分校的隐私保护专家吴新涛、SKT美洲公司的前首席科学家和YesiWell的项目经理David Kil以及HealthMantic的创始人孙俊峰共同开展的跨学科合作他是美国国立卫生研究院的数理统计学家和临床试验设计方面的专家。

DESCRIPTION (provided by applicant): Research in the design and implementation of the SMASH (Semantic Mining of Activity, Social, and Health data) system will address a critical need for data mining tools to help understanding the influence of healthcare social networks, such as YesiWell, on sustained weight loss where the data are multi-dimensional, temporal, semantically heterogeneous, and very sensitive. System design and implementation will rest on five specific aims. The first aim is to develop a novel data mining and statistical learning approach to understand key factors that enable spread of healthy behaviors in a social network (Aim 1). We propose to develop a formal and expressive Semantic Web ontology for the concepts used in describing the semantic features of healthcare data and social networks. We will then bridge the domain knowledge in healthcare and social networks with formal mappings across those ontological concepts (Aim 2). Next, we propose novel recommendation approaches building on top of the influence modeling and prediction. In addition, we will develop methods to utilize the recommendation as a means to better organize the social network such that the adoption of optimal health behaviors in the network can spread quickly and sustainably (Aim 3). To protect the privacy of human subjects during the data mining process for social network and health data, we consider the enforcement of differential privacy through a privacy preserving analysis layer. We will develop novel solutions to preserve differential privacy for mining dynamic health data and social activities of human subjects (Aim 4). To support this research, we will develop a web- accessible portal so that other researchers with little training i data mining will have shared access to data mining tools, ontologies, and social network analysis results (Aim 5). At the end of this project, data resources, tools, ontologies, and technologies will be made available to the larger research community. This work is an inter-disciplinary collaboration among the PI, Dejing Dou, Co-I Daniel Lowd, both experts in data mining and machine learning, and Jessica Greene, an expert in health policy, at the University of Oregon, Brigitte Piniewski MD, the lead of YesiWell, at PeaceHealth Laboratories, Ruoming Jin, an expert in complex network mining, at Kent State University, Xintao Wu, an expert in privacy preserving mining, at the University of North Carolina at Charlotte, David Kil, the previous Chief Scientist at SKT Americas and program manager of YesiWell, and the founder of HealthMantic, and Junfeng Sun, a mathematical statistician at the NIH and an expert in design of clinical trials.