Information extraction from weakly structured radiological reports with natural language queries.

ABSTRACT:

Objectives

Provide physicians and researchers an efficient way to extract information from weakly structured radiology reports with natural language processing (NLP) machine learning models.

Methods

We evaluate seven different German bidirectional encoder representations from transformers (BERT) models on a dataset of 857,783 unlabeled radiology reports and an annotated reading comprehension dataset in the format of SQuAD 2.0 based on 1223 additional reports.

Results

Continued pre-training of a BERT model on the radiology dataset and a medical online encyclopedia resulted in the most accurate model with an F1-score of 83.97% and an exact match score of 71.63% for answerable questions and 96.01% accuracy in detecting unanswerable questions. Fine-tuning a non-medical mo

SUBMITTER: Dada A 

PROVIDER: S-EPMC10791738 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature