Identifying translational indicators and technology opportunities for nanomedical research using tech mining: The case of gold nanostructures
Natalie F. Abrams,
Alan L. Porter,
Donghua Zhu and
Technological Forecasting and Social Change, 2019, vol. 146, issue C, 767-775
Clinical translation of scientific discoveries from bench to bedside is typically a challenging process with sporadic progress along its trajectory. Analyzing R&D can provide key intelligence on advancing biomedical innovation in target domains of interest. In this study, we explore the feasibility of using a streamlined tech mining approach for identification of translational indicators and potential opportunities, using observable markers extracted from selected research literature. We apply this strategy to analyze a set of 23,982 PubMed records that involved gold nanostructures (GNSs) research. Nine indicators are generated to assess what different GNSs research activities had achieved and to predict where GNSs research will likely go. We believe such analysis can provide useful translation intelligence for researchers, funding agencies, and pharmaceutical and biotech companies.
Keywords: Tech mining; Bibliometric analysis; Translational science; Gold nanostructures; Nano-medical research (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:146:y:2019:i:c:p:767-775
Access Statistics for this article
Technological Forecasting and Social Change is currently edited by Fred Phillips
More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Haili He ().