323 应用 MeSH 树状结构和条件到 MeSH 映射来编目和描述临床试验研究重点领域

Winfred Wu, Trevor Yuen, Sakshi Mittal, Rosalina Das, Sheela Dominguez, D. Ransford, Micky Simwenyi
{"title":"323 应用 MeSH 树状结构和条件到 MeSH 映射来编目和描述临床试验研究重点领域","authors":"Winfred Wu, Trevor Yuen, Sakshi Mittal, Rosalina Das, Sheela Dominguez, D. Ransford, Micky Simwenyi","doi":"10.1017/cts.2024.293","DOIUrl":null,"url":null,"abstract":"OBJECTIVES/GOALS: Characterizing and analyzing research studies presents several challenges given the various ways studies may be labeled or organized. The Medical Subject Headings (MeSH) thesaurus is a hierarchical vocabulary that can index and organize research foci using common business intelligence tools to enable rapid exploration of research portfolios. METHODS/STUDY POPULATION: Metadata from ClinicalTrials.gov on 455,437 trials were downloaded and all MeSH terms associated with trials in the condition_browse section were loaded into a database. The corresponding MeSH trees for each term were then identified and mapped to their ancestor terms within the tree. Trials were then indexed based on top four hierarchical levels for each associated MeSH term. Trials performed at the University of Miami (UM) were identified based on locations associated with the trial as well as matching National Clinical Trial (NCT) numbers identified from internal research administration systems. Business intelligence software (Microsoft PowerBI) was applied to the corresponding dataset to enable end user exploration and analysis of the trials within ClinicalTrials.gov. RESULTS/ANTICIPATED RESULTS: A total of 3,271 studies associated with UM were identified, of which, 3,054 (93.3%) had at least one condition MeSH term linked. A total of 7,711 MeSH terms were associated with the trials overall, representing 1,112 unique MeSH terms; the most common terms were carcinoma (164), lymphoma (155), HIV Infections (139), neoplasms (136), and leukemia (122). Utilizing MeSH hierarchy, trials were characterized were categorized into 36 different trees. The most common top tree nodes were neoplasms (1,181), followed by pathological conditions/signs and symptoms (913), immune system diseases (574), nervous system diseases (513), and digestive system diseases (483). Within trees, a total of 184, 681, and 1057 different MeSH terms were specified at the second, third, and fourth nodes in the hierarchy respectively. DISCUSSION/SIGNIFICANCE: Utilizing existing metadata from trials posted on ClinicalTrials.gov and MeSH tree structures can enable organizations to readily explore the foci of clinical trials research. High rates of MeSH term association to research study conditions are necessary to ensure adequate representation of research foci.","PeriodicalId":508693,"journal":{"name":"Journal of Clinical and Translational Science","volume":"356 1","pages":"99 - 100"},"PeriodicalIF":0.0000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"323 Applying MeSH Tree Structures and Condition-to-MeSH Mapping to Catalog and Characterize Clinical Trials Research Focus Areas\",\"authors\":\"Winfred Wu, Trevor Yuen, Sakshi Mittal, Rosalina Das, Sheela Dominguez, D. Ransford, Micky Simwenyi\",\"doi\":\"10.1017/cts.2024.293\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"OBJECTIVES/GOALS: Characterizing and analyzing research studies presents several challenges given the various ways studies may be labeled or organized. The Medical Subject Headings (MeSH) thesaurus is a hierarchical vocabulary that can index and organize research foci using common business intelligence tools to enable rapid exploration of research portfolios. METHODS/STUDY POPULATION: Metadata from ClinicalTrials.gov on 455,437 trials were downloaded and all MeSH terms associated with trials in the condition_browse section were loaded into a database. The corresponding MeSH trees for each term were then identified and mapped to their ancestor terms within the tree. Trials were then indexed based on top four hierarchical levels for each associated MeSH term. Trials performed at the University of Miami (UM) were identified based on locations associated with the trial as well as matching National Clinical Trial (NCT) numbers identified from internal research administration systems. Business intelligence software (Microsoft PowerBI) was applied to the corresponding dataset to enable end user exploration and analysis of the trials within ClinicalTrials.gov. RESULTS/ANTICIPATED RESULTS: A total of 3,271 studies associated with UM were identified, of which, 3,054 (93.3%) had at least one condition MeSH term linked. A total of 7,711 MeSH terms were associated with the trials overall, representing 1,112 unique MeSH terms; the most common terms were carcinoma (164), lymphoma (155), HIV Infections (139), neoplasms (136), and leukemia (122). Utilizing MeSH hierarchy, trials were characterized were categorized into 36 different trees. The most common top tree nodes were neoplasms (1,181), followed by pathological conditions/signs and symptoms (913), immune system diseases (574), nervous system diseases (513), and digestive system diseases (483). Within trees, a total of 184, 681, and 1057 different MeSH terms were specified at the second, third, and fourth nodes in the hierarchy respectively. DISCUSSION/SIGNIFICANCE: Utilizing existing metadata from trials posted on ClinicalTrials.gov and MeSH tree structures can enable organizations to readily explore the foci of clinical trials research. High rates of MeSH term association to research study conditions are necessary to ensure adequate representation of research foci.\",\"PeriodicalId\":508693,\"journal\":{\"name\":\"Journal of Clinical and Translational Science\",\"volume\":\"356 1\",\"pages\":\"99 - 100\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Clinical and Translational Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1017/cts.2024.293\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Clinical and Translational Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/cts.2024.293","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

目标/目的:由于研究的标注或组织方式多种多样,对研究进行定性和分析面临着诸多挑战。医学主题词表(MeSH)是一个分层词汇表,可以使用常见的商业智能工具对研究重点进行索引和组织,从而实现对研究组合的快速探索。方法/研究对象:从 ClinicalTrials.gov 中下载了 455,437 项试验的元数据,并将条件浏览部分中与试验相关的所有 MeSH 术语加载到数据库中。然后确定每个术语对应的 MeSH 树,并将其映射到树中的祖先术语。然后根据每个相关 MeSH 术语的前四个层次对试验进行索引。迈阿密大学(UM)进行的试验是根据与试验相关的地点以及从内部研究管理系统中确定的匹配的国家临床试验(NCT)编号确定的。将商业智能软件(Microsoft PowerBI)应用于相应的数据集,以便最终用户在 ClinicalTrials.gov 中探索和分析试验。结果/预期结果:共发现 3,271 项与 UM 相关的研究,其中 3,054 项(93.3%)至少与一个条件 MeSH 术语相关联。共有 7,711 个 MeSH 术语与试验相关联,代表 1,112 个独特的 MeSH 术语;最常见的术语是癌症(164 个)、淋巴瘤(155 个)、HIV 感染(139 个)、肿瘤(136 个)和白血病(122 个)。利用 MeSH 层次结构,将试验特征分为 36 个不同的树。最常见的顶级树节点是肿瘤(1,181 个),其次是病理条件/体征和症状(913 个)、免疫系统疾病(574 个)、神经系统疾病(513 个)和消化系统疾病(483 个)。在树状结构中,第二、第三和第四节点分别指定了 184、681 和 1057 个不同的 MeSH 术语。讨论/意义:利用临床试验网(ClinicalTrials.gov)上发布的现有试验元数据和 MeSH 树状结构,可以让机构随时探索临床试验研究的重点。为确保充分反映研究重点,有必要提高 MeSH 术语与研究条件的关联率。
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
323 Applying MeSH Tree Structures and Condition-to-MeSH Mapping to Catalog and Characterize Clinical Trials Research Focus Areas
OBJECTIVES/GOALS: Characterizing and analyzing research studies presents several challenges given the various ways studies may be labeled or organized. The Medical Subject Headings (MeSH) thesaurus is a hierarchical vocabulary that can index and organize research foci using common business intelligence tools to enable rapid exploration of research portfolios. METHODS/STUDY POPULATION: Metadata from ClinicalTrials.gov on 455,437 trials were downloaded and all MeSH terms associated with trials in the condition_browse section were loaded into a database. The corresponding MeSH trees for each term were then identified and mapped to their ancestor terms within the tree. Trials were then indexed based on top four hierarchical levels for each associated MeSH term. Trials performed at the University of Miami (UM) were identified based on locations associated with the trial as well as matching National Clinical Trial (NCT) numbers identified from internal research administration systems. Business intelligence software (Microsoft PowerBI) was applied to the corresponding dataset to enable end user exploration and analysis of the trials within ClinicalTrials.gov. RESULTS/ANTICIPATED RESULTS: A total of 3,271 studies associated with UM were identified, of which, 3,054 (93.3%) had at least one condition MeSH term linked. A total of 7,711 MeSH terms were associated with the trials overall, representing 1,112 unique MeSH terms; the most common terms were carcinoma (164), lymphoma (155), HIV Infections (139), neoplasms (136), and leukemia (122). Utilizing MeSH hierarchy, trials were characterized were categorized into 36 different trees. The most common top tree nodes were neoplasms (1,181), followed by pathological conditions/signs and symptoms (913), immune system diseases (574), nervous system diseases (513), and digestive system diseases (483). Within trees, a total of 184, 681, and 1057 different MeSH terms were specified at the second, third, and fourth nodes in the hierarchy respectively. DISCUSSION/SIGNIFICANCE: Utilizing existing metadata from trials posted on ClinicalTrials.gov and MeSH tree structures can enable organizations to readily explore the foci of clinical trials research. High rates of MeSH term association to research study conditions are necessary to ensure adequate representation of research foci.
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
Changes in self-confidence in professional, personal and scientific skills by gender during physician scientist training at the University of Pittsburgh Developing a Bayesian Workshop for Full-time Staff Statisticians Inpatient Screening for Social Determinants of Health: A Quality Improvement Initiative The Epidemiology of Errors in Data Capture, Management, and Analysis: A Scoping Review of Retracted Articles and Retraction Notices in Clinical and Translational Research Interactive Visualization Tool to Understand and Monitor Health Disparities in Diabetes Care and Outcomes
×
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