Utilizing natural language processing to identify cancer-relevant publications at a National Cancer Institute-designated cancer center.

ABSTRACT:

Objectives

The objective of this study was to develop and test natural language processing (NLP) methods for screening and, ultimately, predicting the cancer relevance of peer-reviewed publications.

Materials and methods

Two datasets were used: (1) manually curated publications labeled for cancer relevance, co-authored by members of The University of Kansas Cancer Center (KUCC) and (2) a derived dataset containing cancer-related abstracts from American Association for Cancer Research journals and noncancer-related abstracts from other medical journals. Two text encoding methods were explored: term frequency-inverse document frequency (TF-IDF) vectorization and various BERT embeddings. These representations served as inputs to 3 supervised machine learning classifiers: Suppor

SUBMITTER: Shae W 

PROVIDER: S-EPMC12696645 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature