Transformer-based structuring of free-text radiology report databases.

ABSTRACT:

Objectives

To provide insights for on-site development of transformer-based structuring of free-text report databases by investigating different labeling and pre-training strategies.

Methods

A total of 93,368 German chest X-ray reports from 20,912 intensive care unit (ICU) patients were included. Two labeling strategies were investigated to tag six findings of the attending radiologist. First, a system based on human-defined rules was applied for annotation of all reports (termed "silver labels"). Second, 18,000 reports were manually annotated in 197 h (termed "gold labels") of which 10% were used for testing. An on-site pre-trained model (T_mlm) using masked-language modeling (MLM) was compared to a public, medically pre-trained model (T_med). Both model

SUBMITTER: Nowak S 

PROVIDER: S-EPMC10181962 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature