arXiv
Open Access
2026
Retrieval-Augmented Generation Based Nurse Observation Extraction
Kyomin Hwang
Nojun Kwak
Abstrak
Recent advancements in Large Language Models (LLMs) have played a significant role in reducing human workload across various domains, a trend that is increasingly extending into the medical field. In this paper, we propose an automated pipeline designed to alleviate the burden on nurses by automatically extracting clinical observations from nurse dictations. To ensure accurate extraction, we introduce a method based on Retrieval-Augmented Generation (RAG). Our approach demonstrates effective performance, achieving an F1-score of 0.796 on the MEDIQA-SYNUR test dataset.
Topik & Kata Kunci
Penulis (2)
K
Kyomin Hwang
N
Nojun Kwak
Akses Cepat
Informasi Jurnal
- Tahun Terbit
- 2026
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓