阿片类药物危机研究的交互式网络可视化:加强公共卫生政策研究人员数据链接技能的工具

O. Scrivner, Thuy Nguyen, Michael Ginda, Kosali Simon, Katy Börner
{"title":"阿片类药物危机研究的交互式网络可视化:加强公共卫生政策研究人员数据链接技能的工具","authors":"O. Scrivner, Thuy Nguyen, Michael Ginda, Kosali Simon, Katy Börner","doi":"10.3389/frai.2024.1208874","DOIUrl":null,"url":null,"abstract":"Background Public health policy researchers face a persistent challenge in identifying and integrating relevant data, particularly in the context of the U.S. opioid crisis, where a comprehensive approach is crucial. Purpose To meet this new workforce demand health policy and health economics programs are increasingly introducing data analysis and data visualization skills. Such skills facilitate data integration and discovery by linking multiple resources. Common linking strategies include individual or aggregate level linking (e.g., patient identifiers) in primary clinical data and conceptual linking (e.g., healthcare workforce, state funding, burnout rates) in secondary data. Often, the combination of primary and secondary datasets is sought, requiring additional skills, for example, understanding metadata and constructing interlinkages. Methods To help improve those skills, we developed a 2-step process using a scoping method to discover data and network visualization to interlink metadata. Results: We show how these new skills enable the discovery of relationships among data sources pertinent to public policy research related to the opioid overdose crisis and facilitate inquiry across heterogeneous data resources. In addition, our interactive network visualization introduces (1) a conceptual approach, drawing from recent systematic review studies and linked by the publications, and (2) an aggregate approach, constructed using publicly available datasets and linked through crosswalks. Conclusions These novel metadata visualization techniques can be used as a teaching tool or a discovery method and can also be extended to other public policy domains.","PeriodicalId":508738,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"4 10","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Interactive network visualization of opioid crisis research: a tool for reinforcing data linkage skills for public health policy researchers\",\"authors\":\"O. Scrivner, Thuy Nguyen, Michael Ginda, Kosali Simon, Katy Börner\",\"doi\":\"10.3389/frai.2024.1208874\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background Public health policy researchers face a persistent challenge in identifying and integrating relevant data, particularly in the context of the U.S. opioid crisis, where a comprehensive approach is crucial. Purpose To meet this new workforce demand health policy and health economics programs are increasingly introducing data analysis and data visualization skills. Such skills facilitate data integration and discovery by linking multiple resources. Common linking strategies include individual or aggregate level linking (e.g., patient identifiers) in primary clinical data and conceptual linking (e.g., healthcare workforce, state funding, burnout rates) in secondary data. Often, the combination of primary and secondary datasets is sought, requiring additional skills, for example, understanding metadata and constructing interlinkages. Methods To help improve those skills, we developed a 2-step process using a scoping method to discover data and network visualization to interlink metadata. Results: We show how these new skills enable the discovery of relationships among data sources pertinent to public policy research related to the opioid overdose crisis and facilitate inquiry across heterogeneous data resources. In addition, our interactive network visualization introduces (1) a conceptual approach, drawing from recent systematic review studies and linked by the publications, and (2) an aggregate approach, constructed using publicly available datasets and linked through crosswalks. Conclusions These novel metadata visualization techniques can be used as a teaching tool or a discovery method and can also be extended to other public policy domains.\",\"PeriodicalId\":508738,\"journal\":{\"name\":\"Frontiers in Artificial Intelligence\",\"volume\":\"4 10\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/frai.2024.1208874\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/frai.2024.1208874","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

背景 公共卫生政策研究人员在识别和整合相关数据方面一直面临着挑战,尤其是在美国阿片类药物危机的背景下,采用综合方法至关重要。目的 为满足这一新的劳动力需求,卫生政策和卫生经济学课程正越来越多地引入数据分析和数据可视化技能。这些技能通过连接多种资源来促进数据整合和发现。常见的链接策略包括初级临床数据中的个体或总体级别链接(如患者标识符)和次级数据中的概念链接(如医疗保健劳动力、国家资金、职业倦怠率)。通常情况下,需要将主要数据集和次要数据集结合起来,这就需要额外的技能,例如理解元数据和构建相互链接。方法:为了帮助提高这些技能,我们开发了一个两步流程,使用范围界定法来发现数据,并使用网络可视化来将元数据相互连接起来。结果我们展示了这些新技能如何发现与阿片类药物过量危机相关的公共政策研究数据源之间的关系,以及如何促进跨异构数据资源的查询。此外,我们的交互式网络可视化还引入了(1)一种概念方法,该方法借鉴了近期的系统性综述研究,并通过出版物进行链接;(2)一种汇总方法,该方法利用公开可用的数据集构建,并通过横向联系进行链接。结论 这些新颖的元数据可视化技术可用作教学工具或发现方法,也可扩展到其他公共政策领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Interactive network visualization of opioid crisis research: a tool for reinforcing data linkage skills for public health policy researchers
Background Public health policy researchers face a persistent challenge in identifying and integrating relevant data, particularly in the context of the U.S. opioid crisis, where a comprehensive approach is crucial. Purpose To meet this new workforce demand health policy and health economics programs are increasingly introducing data analysis and data visualization skills. Such skills facilitate data integration and discovery by linking multiple resources. Common linking strategies include individual or aggregate level linking (e.g., patient identifiers) in primary clinical data and conceptual linking (e.g., healthcare workforce, state funding, burnout rates) in secondary data. Often, the combination of primary and secondary datasets is sought, requiring additional skills, for example, understanding metadata and constructing interlinkages. Methods To help improve those skills, we developed a 2-step process using a scoping method to discover data and network visualization to interlink metadata. Results: We show how these new skills enable the discovery of relationships among data sources pertinent to public policy research related to the opioid overdose crisis and facilitate inquiry across heterogeneous data resources. In addition, our interactive network visualization introduces (1) a conceptual approach, drawing from recent systematic review studies and linked by the publications, and (2) an aggregate approach, constructed using publicly available datasets and linked through crosswalks. Conclusions These novel metadata visualization techniques can be used as a teaching tool or a discovery method and can also be extended to other public policy domains.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Using synthetic dataset for semantic segmentation of the human body in the problem of extracting anthropometric data Enhancing educational Q&A systems using a Chaotic Fuzzy Logic-Augmented large language model AI can empower agriculture for global food security: challenges and prospects in developing nations Examining the impact of green technological specialization and the integration of AI technologies on green innovation performance: evidence from China Expandable-RCNN: toward high-efficiency incremental few-shot object detection
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1