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A Bibliometric Analysis on Cancer Population Science with Topic Modeling.

Li DC, Rastegar-Mojarad M, Okamoto J, Liu H, Leichow S - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: We applied two topic modeling techniques: author topic modeling (AT) and dynamic topic modeling (DTM).Our initial results show that AT can address reasonably the issues related to investigators' research interests, research topic distributions and popularities.In compensation, DTM can address the evolving trend of each topic by displaying the proportion changes of key words, which is consistent with the changes of MeSH headings.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN.

ABSTRACT
Bibliometric analysis is a research method used in library and information science to evaluate research performance. It applies quantitative and statistical analyses to describe patterns observed in a set of publications and can help identify previous, current, and future research trends or focus. To better guide our institutional strategic plan in cancer population science, we conducted bibliometric analysis on publications of investigators currently funded by either Division of Cancer Preventions (DCP) or Division of Cancer Control and Population Science (DCCPS) at National Cancer Institute. We applied two topic modeling techniques: author topic modeling (AT) and dynamic topic modeling (DTM). Our initial results show that AT can address reasonably the issues related to investigators' research interests, research topic distributions and popularities. In compensation, DTM can address the evolving trend of each topic by displaying the proportion changes of key words, which is consistent with the changes of MeSH headings.

No MeSH data available.


Related in: MedlinePlus

Workflow of the framework
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f1-2091979: Workflow of the framework

Mentions: The overall components and steps of the framework are illustrated in Figure 1. The workflow consists of six steps of information processing. Via Retrieve publications queried by PI, abstracts of both PIs of DCP and DCCPS are retrieved from PubMed search engine. Author affiliations are employed to remove duplications of author names (we ignore the few overlapping of PIs from both sources for now). Since in this study, we are only concerned about all PIs, we just assume that each article was written only by PIs and co-authors are ignored for now. The second step aims at preprocessing the data. For each document, we remove stop words and filter out words such as cancer, which appear in almost in each abstract, based on Term Frequency-Inverse Document Frequency (TF-IDF).


A Bibliometric Analysis on Cancer Population Science with Topic Modeling.

Li DC, Rastegar-Mojarad M, Okamoto J, Liu H, Leichow S - AMIA Jt Summits Transl Sci Proc (2015)

Workflow of the framework
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525228&req=5

f1-2091979: Workflow of the framework
Mentions: The overall components and steps of the framework are illustrated in Figure 1. The workflow consists of six steps of information processing. Via Retrieve publications queried by PI, abstracts of both PIs of DCP and DCCPS are retrieved from PubMed search engine. Author affiliations are employed to remove duplications of author names (we ignore the few overlapping of PIs from both sources for now). Since in this study, we are only concerned about all PIs, we just assume that each article was written only by PIs and co-authors are ignored for now. The second step aims at preprocessing the data. For each document, we remove stop words and filter out words such as cancer, which appear in almost in each abstract, based on Term Frequency-Inverse Document Frequency (TF-IDF).

Bottom Line: We applied two topic modeling techniques: author topic modeling (AT) and dynamic topic modeling (DTM).Our initial results show that AT can address reasonably the issues related to investigators' research interests, research topic distributions and popularities.In compensation, DTM can address the evolving trend of each topic by displaying the proportion changes of key words, which is consistent with the changes of MeSH headings.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN.

ABSTRACT
Bibliometric analysis is a research method used in library and information science to evaluate research performance. It applies quantitative and statistical analyses to describe patterns observed in a set of publications and can help identify previous, current, and future research trends or focus. To better guide our institutional strategic plan in cancer population science, we conducted bibliometric analysis on publications of investigators currently funded by either Division of Cancer Preventions (DCP) or Division of Cancer Control and Population Science (DCCPS) at National Cancer Institute. We applied two topic modeling techniques: author topic modeling (AT) and dynamic topic modeling (DTM). Our initial results show that AT can address reasonably the issues related to investigators' research interests, research topic distributions and popularities. In compensation, DTM can address the evolving trend of each topic by displaying the proportion changes of key words, which is consistent with the changes of MeSH headings.

No MeSH data available.


Related in: MedlinePlus