亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Topic analysis on publications and patents toward fully automated translational science benefits model impact extraction

萃取(化学) 数据科学 计算机科学 化学 色谱法
作者
T. C. Manjunath,Eline Appelmans,Sinem Balta,Dominick DiMercurio,Claudia Avalos,Karen Stark
出处
期刊:Frontiers in Research Metrics and Analytics [Frontiers Media]
卷期号:10
标识
DOI:10.3389/frma.2025.1596687
摘要

The Clinical and Translational Science Award (CTSA) program, funded by the National Center for Advancing Translational Sciences (NCATS), has supported over 65 hubs, generating 118,490 publications from 2006 to 2021. Measuring the impact of these outputs remains challenging, as traditional bibliometric methods fail to capture patents, policy contributions, and clinical implementation. The Translational Science Benefits Model (TSBM) provides a structured framework for assessing clinical, community, economic, and policy benefits, but its manual application is resource-intensive. Advances in Natural Language Processing (NLP) and Artificial Intelligence (AI) offer a scalable solution for automating benefit extraction from large research datasets. This study presents an NLP-driven pipeline that automates the extraction of TSBM benefits from research outputs using Latent Dirichlet Allocation (LDA) topic modeling to enable efficient, scalable, and reproducible impact analysis. The application of NLP allows the discovery of topics and benefits to emerge from the very large corpus of CTSA documents without requiring directed searches or preconceived benefits for data mining. We applied LDA topic modeling to publications, patents, and grants and mapped the topics to TSBM benefits using subject matter expert (SME) validation. Impact visualizations, including heatmaps and t-SNE plots, highlighted benefit distributions across the corpus and CTSA hubs. Spanning CTSA hub grants awarded from 2006 to 2023, our analysis corpus comprised 1,296 projects, 127,958 publications and 352 patents. Applying our NLP-driven pipeline to deduplicated data, we found that clinical and community benefits were the most frequently extracted benefits from publications and projects, reflecting the patient-centered and community-driven nature of CTSA research. Economic and policy benefits were less frequently identified, prompting the inclusion of patent data to better capture commercialization impacts. The Publications LDA Model proved the most effective for benefit extraction for publications and projects. All patents were automatically tagged as economic benefits, given their intrinsic focus on commercialization and in accordance with TSBM guidelines. Automated NLP-driven benefit extraction enabled a data-driven approach to applying the TSBM at the scale of the entire CTSA program outputs.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
科研通AI6应助闪闪飞机采纳,获得10
2秒前
chloe完成签到,获得积分10
4秒前
33发布了新的文献求助10
5秒前
11秒前
王娅楠完成签到,获得积分10
13秒前
13秒前
王娅楠发布了新的文献求助10
16秒前
jiayu123发布了新的文献求助10
16秒前
jiayu123完成签到,获得积分20
22秒前
李健应助王娅楠采纳,获得10
26秒前
41秒前
33发布了新的文献求助10
47秒前
jyy应助科研通管家采纳,获得10
1分钟前
JamesPei应助Yike采纳,获得10
1分钟前
33发布了新的文献求助10
1分钟前
送人头完成签到,获得积分10
2分钟前
闪闪飞机完成签到,获得积分10
2分钟前
过时的手套完成签到,获得积分10
2分钟前
科研通AI6应助过时的手套采纳,获得10
2分钟前
赘婿应助冰冰采纳,获得10
2分钟前
彩虹儿应助33采纳,获得10
2分钟前
邢夏之完成签到 ,获得积分10
3分钟前
唐泽雪穗完成签到,获得积分10
3分钟前
jyy应助科研通管家采纳,获得10
3分钟前
jyy应助科研通管家采纳,获得10
3分钟前
量子星尘发布了新的文献求助10
4分钟前
5分钟前
彩虹儿应助诚心山灵采纳,获得10
5分钟前
彩虹儿应助诚心山灵采纳,获得10
5分钟前
fufufu123完成签到 ,获得积分10
5分钟前
机智的孤兰完成签到 ,获得积分10
6分钟前
冰冰发布了新的文献求助10
6分钟前
Survivor完成签到,获得积分10
7分钟前
lovelife完成签到,获得积分10
8分钟前
cqhecq完成签到,获得积分10
9分钟前
cherry完成签到,获得积分10
9分钟前
Kevin完成签到,获得积分10
9分钟前
cherry发布了新的文献求助10
10分钟前
我是老大应助33采纳,获得10
11分钟前
11分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
二维材料在应力作用下的力学行为和层间耦合特性研究 600
苯丙氨酸解氨酶的祖先序列重建及其催化性能 500
Schifanoia : notizie dell'istituto di studi rinascimentali di Ferrara : 66/67, 1/2, 2024 470
Effects of different anesthesia methods on bleeding and prognosis in endoscopic sinus surgery: a meta-analysis and systematic review of randomized controlled trials 400
Laboratory Animal Technician TRAINING MANUAL WORKBOOK 2012 edtion 400
Progress and Regression 400
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 内科学 生物化学 物理 计算机科学 纳米技术 遗传学 基因 复合材料 化学工程 物理化学 病理 催化作用 免疫学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 4844734
求助须知:如何正确求助?哪些是违规求助? 4144938
关于积分的说明 12833849
捐赠科研通 3891632
什么是DOI,文献DOI怎么找? 2139250
邀请新用户注册赠送积分活动 1159267
关于科研通互助平台的介绍 1059558