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

实施和评估用于 YMSM 吸毒和性接触网络中实体解析的机器学习工具

基本信息

批准号：
9348928
负责人：
Patrick Francis Janulis
金额：
$ 21.91万
依托单位：
NORTHWESTERN UNIVERSITY AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-30 至 2019-09-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9348928
关键词：
AIDS prevention Age Algorithms Area Behavioral Big Data Computer software Data Data Collection Data Set Databases Development Drug usage Epidemiology Foundations Funding Future Goals Graph HIV Human Incidence Individual Infection Intervention Intervention Studies Interview Intuition Investigation Knowledge Link Location Logic Machine Learning Manuals Measurement Methodology Methods Modeling Modernization Molecular Molecular Epidemiology Names National Institute of Drug Abuse Nature Output Pathway Analysis Performance Phylogenetic Analysis Phylogeny Population Prevention Research Process Public Health Race Research Research Personnel Resolution Resources Sampling Statistical Models Structure Subgroup Surveys System Testing Time Training Trees United States National Institutes of Health Universities Work computer human interaction computer science data management disease transmission drug structure epidemiologic data experience graphical user interface high risk population improved innovation men who have sex with men novel open source outreach response sex social success surveillance data tool touchscreen transmission process usability user-friendly young men who have sex with men

项目摘要

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.

项目概要将接触网络数据与分子监测数据整合的努力为艾滋病毒提供了巨大的希望跟踪和干预。然而，缺乏促进综合分子社会监测的工具仍然是取得进展的重大障碍。例如，大多数联系网络数据仅包含以下信息：一个人的直接性伴侣和吸毒伴侣。然而，相同的合作伙伴可以出现在不同的地方多个人的联系网络。因此，合作伙伴必须通过联系网络进行匹配 - 称为实体解析 (ER) 的过程 - 以便提供整个联系网络的准确视图结构。目前，ER 流程需要大量资源来手动匹配个人，或者在编程方面拥有丰富的技术专业知识，可以使用概率更有效地匹配个人模型。因此，该项目将 1）开发一种机器学习算法来匹配跨领域的个体个人联系网络，并使用大量现有的男男性行为年轻男性数据集对其进行验证， 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（网络画布）。该项目的结果将提供一个开放的来源和免费提供的工具，可以大大减少跨联系人匹配个人的障碍网络，从而为研究人员和公共卫生官员提供不受阻碍的访问吸毒和性网络的基本结构，以及整合接触网络数据的有效工具与分子监测。