Fine-tuning of language models for automated structuring of medical exam reports to improve patient screening and analysis
Abstract The analysis of medical imaging reports is labour-intensive but crucial for accurate diagnosis and effective patient screening. Often presented as unstructured text, these reports require systematic organisation for efficient interpretation. This study applies Natural Language Processing (N...
Saved in:
| Main Authors: | Luis B. Elvas, Rafaela Santos, João C. Ferreira |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-05695-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
B-NER: A Novel Bangla Named Entity Recognition Dataset With Largest Entities and Its Baseline Evaluation
by: Md. Zahidul Haque, et al.
Published: (2023-01-01) -
Advancements in Arabic Named Entity Recognition: A Comprehensive Review
by: Taoufiq El Moussaoui, et al.
Published: (2024-01-01) -
OdNER: NER resource creation and system development for low-resource Odia language
by: Tusarkanta Dalai, et al.
Published: (2025-06-01) -
Weakly-Supervised Multilingual Medical NER for Symptom Extraction for Low-Resource Languages
by: Rigon Sallauka, et al.
Published: (2025-05-01) -
Corpus Development and NER Model for Identification of Legal Entities (Articles, Laws, and Sanctions) in Corruption Court Decisions in Indonesia
by: Edy Subowo, et al.
Published: (2025-06-01)