Utilizing natural language processing to identify cancer-relevant publications at a National Cancer Institute-designated cancer center
Overall, our results suggest that NLP can effectively automate the classification of cancer-relevant publications, enhancing research productivity tracking; however, great care should be taken in selecting the appropriate data, text representation approach, and machine learning approach.
Authors
- Dong Pei (ORCID: https://orcid.org/0000-0002-8367-729X)
- Whitney Shae
- Dinesh Pal Mudaranthakam (ORCID: https://orcid.org/0000-0001-9767-1158)
- Jeffrey Thompson (ORCID: https://orcid.org/0000-0002-0876-2582)
- Devin C. Koestler (ORCID: https://orcid.org/0000-0002-0598-0146)
- John E. Fife
- Lisa M. Harlan-Williams (ORCID: https://orcid.org/0000-0003-4612-8192)
- M Saif
Institutions
- The University of Kansas Cancer Center (US)
- University of Kansas Medical Center (US)
Publication Details
- Journal
- JAMIA Open
- Published
- 2025-11-03
- DOI
- https://doi.org/10.1093/jamiaopen/ooaf156
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Kansas IDeA Network of Biomedical Research Excellence
- National Cancer Institute