A deep learning approach for identifying biomedical breakthrough discoveries using context analysis

Xue Wang, Xuemei Yang, Jian Du, Xuwen Wang, Jiao Li and Xiaoli Tang ()
Additional contact information
Xue Wang: Chinese Academy of Medical Sciences and Peking Union Medical College
Xuemei Yang: Chinese Academy of Medical Sciences and Peking Union Medical College
Jian Du: Peking University
Xuwen Wang: Chinese Academy of Medical Sciences and Peking Union Medical College
Jiao Li: Chinese Academy of Medical Sciences and Peking Union Medical College
Xiaoli Tang: Chinese Academy of Medical Sciences and Peking Union Medical College

Scientometrics, 2021, vol. 126, issue 7, No 7, 5549 pages

Abstract: Abstract Breakthrough research in scientific fields usually comes as a manifestation of major development and advancement. These advances build to an epiphany where new ways of thinking about a problem become possible. Identifying breakthrough research can be useful for cultivating and funding further innovation. This article presents a new method for identifying scientific breakthroughs from research papers based on cue words commonly associated with major advancements. We looked for specific terms signifying scientific breakthroughs in citing sentences to identify breakthrough articles. By setting a threshold for the number of citing sentences (“citances”) with breakthrough cue words that peer scholars often use when evaluating research, we identified articles containing breakthrough research. We call this approach the “others-evaluation” process. We then shortlisted candidates from the selected articles based on the authors’ evaluations of their own research, found in the abstracts. This we call the “self-evaluation” process. Combining the two approaches into a dual “others-self” evaluation process, we arrived at a sample of 237 potential breakthrough articles, most of which are recommended by the Faculty Opinions. Based on the breakthrough articles identified, using SVM, TextCNN, and BERT to train the models to identify abstracts with breakthrough evaluations. This automatic identification model can greatly simplify the process of others-self-evaluation process and promote identifying breakthrough research.

Keywords: Deep learning; Context analysis; Breakthrough discoveries; Breakthrough identification; Citances (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-021-04003-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:126:y:2021:i:7:d:10.1007_s11192-021-04003-z

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-021-04003-z

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().