An End-to-End Natural Language Processing System for Automatically Extracting Radiation Therapy Events From Clinical Texts.

ABSTRACT:

Purpose

Real-world evidence for radiation therapy (RT) is limited because it is often documented only in the clinical narrative. We developed a natural language processing system for automated extraction of detailed RT events from text to support clinical phenotyping.

Methods and materials

A multi-institutional data set of 96 clinician notes, 129 North American Association of Central Cancer Registries cancer abstracts, and 270 RT prescriptions from HemOnc.org was used and divided into train, development, and test sets. Documents were annotated for RT events and associated properties: dose, fraction frequency, fraction number, date, treatment site, and boost. Named entity recognition models for properties were developed by fine-tuning BioClinicalBERT and RoBERTa transformer m

SUBMITTER: Bitterman DS 

PROVIDER: S-EPMC10522797 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature