Implementing and Evaluating a Machine Learning Tool for Entity Resolution in Drug Use and Sexual Contact Networks of YMSM

项目摘要 将接触网络数据与分子监测数据相结合的努力为艾滋病毒提供了巨大的希望 跟踪和干预。然而，缺乏工具，以促进综合分子社会监测 仍然是取得进展的重大障碍。例如，大多数联系人网络数据仅包含以下信息： 一个人的直接性和吸毒伙伴。然而，同样的伙伴可以出现在 多个人的联系网络。因此，合作伙伴必须通过联系网络进行匹配- a 称为实体解析（ER）的过程-为了提供整个联系网络的准确视图 结构ER的过程目前需要大量的资源来手动匹配个体， 在编程方面具有相当的技术专长，可以更有效地使用概率 模型因此，该项目将：1）开发一种机器学习算法， 个人联系网络，并使用现有的大量男同性恋青年数据集进行验证， 以及2）创建图形用户界面以将算法实现为现有工具的附加包 用于网络数据捕获和处理（Network Canvas）。该项目的成果将提供一个开放的- 可以大大减少在联系人之间匹配个人障碍 网络，从而为研究人员和公共卫生官员提供不受阻碍的访问， 毒品使用和性网络的基本结构，以及整合接触网络数据的有力工具 进行分子监测

Project Summary Efforts to integrate contact network data with molecular surveillance data provide enormous promise for HIV tracking and intervention. However, the lack of tools to facilitate integrated molecular-social surveillance remains a substantial barrier to progress. For example, most contact network data only contains information on the immediate sexual and drug use partners of a single individual. Yet, the same partners can appear across the contact networks of multiple individuals. Therefore, partners must be matched across contact networks - a process called entity resolution (ER) - in order to provide an accurate view of the overall contact network structure. The process of ER currently requires either substantial resources to manually match individuals or considerable technological expertise in programming to more efficiently match individuals using probabilistic models. Accordingly, this project will 1) develop a machine learning algorithm to match individuals across personal contact networks and validate it using a large existing dataset of young men who have sex with men, and 2) create a graphical user interface to implement the algorithm as an add-on package to an existing tool for network data capture and processing (Network Canvas). The results of this project will provide an open- source and freely available tool that can drastically reduce barriers to matching individuals across contact networks, thereby providing researchers and public health officials with unencumbered access to the underlying structure of drug use and sexual networks, and a potent tool for integrating contact network data with molecular surveillance.