The natural language processing of radiology requests and reports of chest imaging: Comparing five transformer models' multilabel classification and a proof-of-concept study.
Health Informatics J
; 28(4): 14604582221131198, 2022.
Article
in English
| MEDLINE | ID: covidwho-2064628
ABSTRACT
BACKGROUND:
Radiology requests and reports contain valuable information about diagnostic findings and indications, and transformer-based language models are promising for more accurate text classification.METHODS:
In a retrospective study, 2256 radiologist-annotated radiology requests (8 classes) and reports (10 classes) were divided into training and testing datasets (90% and 10%, respectively) and used to train 32 models. Performance metrics were compared by model type (LSTM, Bertje, RobBERT, BERT-clinical, BERT-multilingual, BERT-base), text length, data prevalence, and training strategy. The best models were used to predict the remaining 40,873 cases' categories of the datasets of requests and reports.RESULTS:
The RobBERT model performed the best after 4000 training iterations, resulting in AUC values ranging from 0.808 [95% CI (0.757-0.859)] to 0.976 [95% CI (0.956-0.996)] for the requests and 0.746 [95% CI (0.689-0.802)] to 1.0 [95% CI (1.0-1.0)] for the reports. The AUC for the classification of normal reports was 0.95 [95% CI (0.922-0.979)]. The predicted data demonstrated variability of both diagnostic yield for various request classes and request patterns related to COVID-19 hospital admission data.CONCLUSION:
Transformer-based natural language processing is feasible for the multilabel classification of chest imaging request and report items. Diagnostic yield varies with the information in the requests.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Radiology
/
COVID-19
Type of study:
Diagnostic study
/
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Health Informatics J
Year:
2022
Document Type:
Article
Affiliation country:
14604582221131198
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