دورية أكاديمية
Multilingual RECIST classification of radiology reports using supervised learning.
| العنوان: | Multilingual RECIST classification of radiology reports using supervised learning. |
|---|---|
| المؤلفون: | Mottin, Luc, Goldman, Jean-Philippe, Jäggli, Christoph, Achermann, Rita, Gobeill, Julien, Knafou, Julien, Ehrsam, Julien, Wicky, Alexandre, Gérard, Camille L, Schwenk, Tanja, Charrier, Mélinda, Tsantoulis, Petros, Lovis, Christian, Leichtle, Alexander, Kiessling, Michael K, Michielin, Olivier, Pradervand, Sylvain, Foufi, Vasiliki, Ruch, Patrick |
| المصدر: | Mottin, Luc; Goldman, Jean-Philippe; Jäggli, Christoph; Achermann, Rita; Gobeill, Julien; Knafou, Julien; Ehrsam, Julien; Wicky, Alexandre; Gérard, Camille L; Schwenk, Tanja; Charrier, Mélinda; Tsantoulis, Petros; Lovis, Christian; Leichtle, Alexander; Kiessling, Michael K; Michielin, Olivier; Pradervand, Sylvain; Foufi, Vasiliki; Ruch, Patrick (2023). Multilingual RECIST classification of radiology reports using supervised learning. Frontiers in digital health, 5(1195017), p. 1195017. Frontiers Media 10.3389/fdgth.2023.1195017 |
| بيانات النشر: | Frontiers Media |
| سنة النشر: | 2023 |
| المجموعة: | BORIS (Bern Open Repository and Information System, University of Bern) |
| مصطلحات موضوعية: | 610 Medicine & health |
| الوصف: | OBJECTIVES The objective of this study is the exploration of Artificial Intelligence and Natural Language Processing techniques to support the automatic assignment of the four Response Evaluation Criteria in Solid Tumors (RECIST) scales based on radiology reports. We also aim at evaluating how languages and institutional specificities of Swiss teaching hospitals are likely to affect the quality of the classification in French and German languages. METHODS In our approach, 7 machine learning methods were evaluated to establish a strong baseline. Then, robust models were built, fine-tuned according to the language (French and German), and compared with the expert annotation. RESULTS The best strategies yield average F1-scores of 90% and 86% respectively for the 2-classes (Progressive/Non-progressive) and the 4-classes (Progressive Disease, Stable Disease, Partial Response, Complete Response) RECIST classification tasks. CONCLUSIONS These results are competitive with the manual labeling as measured by Matthew's correlation coefficient and Cohen's Kappa (79% and 76%). On this basis, we confirm the capacity of specific models to generalize on new unseen data and we assess the impact of using Pre-trained Language Models (PLMs) on the accuracy of the classifiers. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| العلاقة: | https://boris.unibe.ch/184295Test/ |
| الإتاحة: | https://doi.org/10.3389/fdgth.2023.1195017Test https://boris.unibe.ch/184295/1/fdgth-05-1195017.pdfTest https://boris.unibe.ch/184295Test/ |
| حقوق: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.F0E8D582 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |