Adverse event signal extraction from cancer patients' narratives focusing on impact on their daily-life activities.

ABSTRACT: Adverse event (AE) management is important to improve anti-cancer treatment outcomes, but it is known that some AE signals can be missed during clinical visits. In particular, AEs that affect patients' activities of daily living (ADL) need careful monitoring as they may require immediate medical intervention. This study aimed to build deep-learning (DL) models for extracting signals of AEs limiting ADL from patients' narratives. The data source was blog posts written in Japanese by breast cancer patients. After pre-processing and annotation for AE signals, three DL models (BERT, ELECTRA, and T5) were trained and tested in three different approaches for AE signal identification. The performances of the trained models were evaluated in terms of precision, recall, and F1 scores. From 2,272 bl

SUBMITTER: Nishioka S 

PROVIDER: S-EPMC10509234 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature