Biomedical literature-based clinical phenotype definition discovery using large language models.

ABSTRACT: Electronic health record (EHR) phenotyping is a high-demand task because most phenotypes are not usually readily defined. The objective of this study is to develop an effective text-mining approach that automatically extracts clinical phenotype definitions-related sentences from biomedical literature. Abstract-level and full-text sentence-level classifiers were developed for clinical phenotype discovery from PubMed. We compared the performance of the abstract-level classifier on machine learning algorithms: support vector machine (SVM), logistic regression (LR), naïve Bayes, and decision tree. SVM classifier showed the best performance (F-measure = 98%) in identifying clinical phenotype-relevant abstracts. It predicted 459 406 clinical phenotype-related abstracts. For the full-text sentenc

SUBMITTER: Binkheder S 

PROVIDER: S-EPMC12462612 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature