Retrieval-Augmented Generation Based Nurse Observation Extraction
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Retrieval-Augmented Large Language Models for Schema-Constrained Clinical Information Extraction
Conversational nurse-patient transcripts contain actionable observations, but converting these transcripts into structured representations at scale remains challenging. Documentation burden is substantial, with prior stu…
Information ExtractionEmpowering Healthcare Practitioners with Language Models: Structuring Speech Transcripts in Two Real-World Clinical Applications
Large language models (LLMs) such as GPT-4o and o1 have demonstrated strong performance on clinical natural language processing (NLP) tasks across multiple medical benchmarks. Nonetheless, two high-impact NLP tasks - str…
End-to-End Trainable Retrieval-Augmented Generation for Relation Extraction
This paper addresses a crucial challenge in retrieval-augmented generation-based relation extractors; the end-to-end training is not applicable to conventional retrieval-augmented generation due to the non-differentiable…
RelationRelation ExtractionRetrievalRetrieval-augmented Generation+1Retrieval-Augmented Generative Question Answering for Event Argument Extraction
Event argument extraction has long been studied as a sequential prediction problem with extractive-based methods, tackling each argument in isolation. Although recent work proposes generation-based methods to capture cro…
Event Argument ExtractionFew-Shot LearningGenerative Question AnsweringQuestion Answering+1Fast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom Monitoring
Data for human-human spoken dialogues for research and development are currently very limited in quantity, variety, and sources; such data are even scarcer in healthcare. In this work, we investigate fast prototyping of …
Retrieval