Journal
NATURE BIOTECHNOLOGY
Volume 36, Issue 10, Pages 937-945Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/nbt.4267
Keywords
-
Categories
Funding
- NIH Intramural Research Program, National Library of Medicine
Ask authors/readers for more resources
PubMed is a widely used search engine for biomedical literature. It is developed and maintained by the US National Library of Medicine/National Center for Biotechnology Information and is visited daily by millions of users around the world. For decades, PubMed has used advanced artificial intelligence technologies that extract patterns of collective user activity, such as machine learning and natural language processing, to inform the algorithmic changes that ultimately improve a user's search experience. Although these efforts have led to objective improvements in search quality, the technical underpinnings remain largely invisible and go largely unnoticed by most users. Here we describe how these 'under-the-hood' techniques work within PubMed and report how their effectiveness and usage is assessed in real-world scenarios. In doing so, we hope to increase the transparency of the PubMed system and enable users to make more effective use of the search engine. We also identify open challenges and new opportunities for computational researchers to explore the potential of future improvements.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available