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RTEX: A novel framework for ranking, tagging, and explanatory diagnostic captioning of radiography exams
- Source :
- Journal of the American Medical Informatics Association : JAMIA
- Publication Year :
- 2021
- Publisher :
- Oxford University Press, 2021.
-
Abstract
- Objective The study sought to assist practitioners in identifying and prioritizing radiography exams that are more likely to contain abnormalities, and provide them with a diagnosis in order to manage heavy workload more efficiently (eg, during a pandemic) or avoid mistakes due to tiredness. Materials and Methods This article introduces RTEx, a novel framework for (1) ranking radiography exams based on their probability to be abnormal, (2) generating abnormality tags for abnormal exams, and (3) providing a diagnostic explanation in natural language for each abnormal exam. Our framework consists of deep learning and retrieval methods and is assessed on 2 publicly available datasets. Results For ranking, RTEx outperforms its competitors in terms of nDCG@k. The tagging component outperforms 2 strong competitor methods in terms of F1. Moreover, the diagnostic captioning component, which exploits the predicted tags to constrain the captioning process, outperforms 4 captioning competitors with respect to clinical precision and recall. Discussion RTEx prioritizes abnormal exams toward the improvement of the healthcare workflow by introducing a ranking method. Also, for each abnormal radiography exam RTEx generates a set of abnormality tags alongside a diagnostic text to explain the tags and guide the medical expert. Human evaluation of the produced text shows that employing the generated tags offers consistency to the clinical correctness and that the sentences of each text have high clinical accuracy. Conclusions This is the first framework that successfully combines 3 tasks: ranking, tagging, and diagnostic captioning with focus on radiography exams that contain abnormalities.
- Subjects :
- 0301 basic medicine
Closed captioning
Diagnostic Imaging
computer-assisted diagnosis
AcademicSubjects/SCI01060
Computer science
education
Health Informatics
computer.software_genre
Research and Applications
information storage and retrieval
030218 nuclear medicine & medical imaging
Ranking (information retrieval)
Workflow
03 medical and health sciences
Consistency (database systems)
0302 clinical medicine
explainability
Humans
AcademicSubjects/MED00580
Language
business.industry
Deep learning
deep learning
diagnostic captioning
Radiography
030104 developmental biology
Artificial intelligence
Abnormality
AcademicSubjects/SCI01530
Precision and recall
business
computer
Natural language
Natural language processing
Subjects
Details
- Language :
- English
- ISSN :
- 1527974X and 10675027
- Volume :
- 28
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- Journal of the American Medical Informatics Association : JAMIA
- Accession number :
- edsair.doi.dedup.....a8b83f679bdc61c3e4c96f3d9c6458a2