@inproceedings{b2ea46a1025341d4b5e53beb93a1b857,
title = "An author topic analysis of tobacco regulation investigators",
abstract = "To facilitate the implementation of the Family Smoking Prevention and Tobacco Control Act of 2009, the Federal Drug Agency (FDA) Center for Tobacco Products (CTP) has identified research priorities under the umbrella of tobacco regulatory science (TRS). As a newly introduced field, the current landscape of TRS research is unclear. In this work, we conducted a bibliometric study of TRS research by applying author topic modeling on MEDLINE citations published by currently-funded TRS principle investigators. Our initial results show that author topic modeling can address the issue of research interests reasonably. Furthermore, a network involving authors, topics and words can be established for more detailed bibliometric analysis. This network may also be useful to grantees and funding administrators in suggesting potential collaborators or identifying those that share common research interests for data harmonization or other purposes.",
author = "Li, {Ding Cheng} and Janet Okamoto and Scott Leischow and Hongfang Liu",
note = "Funding Information: We obtained all MedLINE citations published by the principle investigators (PIs, 133 in total) of TRS grants funded by the CTP through Tobacco Regulatory Science Research Program (TRSP) ( http://prevention.nih.gov/tobacco/portfolio.aspx ). Since each article can have multiple authors, the author set considered here are PIs (can appear in any place in the paper) plus the last author of the paper. The final author set includes 2,740 authors. The document set includes those MEDLINE citations with abstract available, resulting in 7460 abstracts. Funding Information: This study was made possible by National Science Foundation ABI:0845523, National Institute of Health R01LM009959A1 and R01GM102283A1. Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; International Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014 ; Conference date: 13-05-2014 Through 16-05-2014",
year = "2014",
doi = "10.1007/978-3-319-13186-3_55",
language = "English (US)",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "616--627",
editor = "Wen-Chih Peng and Haixun Wang and Zhi-Hua Zhou and Ho, {Tu Bao} and Tseng, {Vincent S.} and Chen, {Arbee L.P.} and James Bailey",
booktitle = "Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops",
}