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数据挖掘工具的关键需求，以帮助理解医疗保健社交网络（如YesiWell）对持续减肥的影响，其中数据是多维的、时间的、语义异构的，并且非常敏感。系统的设计和实施将基于五个具体目标。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会网络中传播的关键因素（目标1）。我们建议为用于描述医疗保健数据和社交网络的语义特征的概念开发一个正式的、表达性的语义Web本体。然后，我们将在医疗保健和社交网络领域知识与这些本体概念的正式映射之间架起桥梁（目标2）。接下来，我们在影响建模和预测的基础上提出了新的推荐方法。此外，我们将开发方法，利用该建议作为更好地组织社会网络的手段，以便在网络中采用最佳健康行为可以快速和可持续地传播（目标3）。为了在社交网络和健康数据的数据挖掘过程中保护人类受试者的隐私，我们考虑通过隐私保护分析层强制执行差分隐私。我们将开发新的解决方案，以保护挖掘人类受试者动态健康数据和社会活动的差异隐私（目标4）。为了支持这项研究，我们将开发一个可访问的门户网站，以便其他没有受过数据挖掘训练的研究人员可以共享访问数据挖掘工具、本体和社会网络分析结果（目标5）。在这个项目结束时，数据资源、工具、本体和技术将提供给更大的研究社区。这项工作是一项跨学科合作，其中包括PI， Dejing Dou, Co-I Daniel Lowd，他们都是数据挖掘和机器学习方面的专家，俄勒冈大学的卫生政策专家Jessica Greene，和平健康实验室YesiWell的负责人Brigitte Piniewski医学博士，肯特州立大学复杂网络挖掘专家ruming Jin，北卡罗莱纳大学夏洛特分校隐私保护挖掘专家Xintao Wu，SKT Americas前首席科学家、YesiWell项目经理、HealthMantic创始人，以及美国国立卫生研究院数理统计学家、临床试验设计专家孙俊峰。

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.