An author topic analysis of tobacco regulation investigators

Ding Cheng Li, Janet Okamoto, Scott Leischow, Hongfang D Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8643
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

OtherInternational 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
CountryTaiwan, Province of China
CityTainan
Period5/13/145/16/14

Fingerprint

Tobacco
Bibliometrics
Smoking
Citations
Modeling
Drugs

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Li, D. C., Okamoto, J., Leischow, S., & Liu, H. D. (2014). An author topic analysis of tobacco regulation investigators. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8643, pp. 616-627). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8643). Springer Verlag. https://doi.org/10.1007/978-3-319-13186-3_55

An author topic analysis of tobacco regulation investigators. / Li, Ding Cheng; Okamoto, Janet; Leischow, Scott; Liu, Hongfang D.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8643 Springer Verlag, 2014. p. 616-627 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8643).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Li, DC, Okamoto, J, Leischow, S & Liu, HD 2014, An author topic analysis of tobacco regulation investigators. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 8643, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8643, Springer Verlag, pp. 616-627, 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, Tainan, Taiwan, Province of China, 5/13/14. https://doi.org/10.1007/978-3-319-13186-3_55
Li DC, Okamoto J, Leischow S, Liu HD. An author topic analysis of tobacco regulation investigators. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8643. Springer Verlag. 2014. p. 616-627. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-13186-3_55
Li, Ding Cheng ; Okamoto, Janet ; Leischow, Scott ; Liu, Hongfang D. / An author topic analysis of tobacco regulation investigators. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8643 Springer Verlag, 2014. pp. 616-627 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{cde0887ec1c148dc97d77289840fe647,
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 Liu, {Hongfang D}",
year = "2014",
doi = "10.1007/978-3-319-13186-3_55",
language = "English (US)",
isbn = "9783319131856",
volume = "8643",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "616--627",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - An author topic analysis of tobacco regulation investigators

AU - Li, Ding Cheng

AU - Okamoto, Janet

AU - Leischow, Scott

AU - Liu, Hongfang D

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84915782111&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84915782111&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-13186-3_55

DO - 10.1007/978-3-319-13186-3_55

M3 - Conference contribution

AN - SCOPUS:84915782111

SN - 9783319131856

VL - 8643

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 616

EP - 627

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag

ER -