Dec 4, 2023 · In this paper, we propose an approach that combines Large Language Models (LLMs) with human expertise to create an efficient method for ...

The unstructured nature of clinical notes within electronic health records often conceals vital patient-related information, making it challeng-.

Our study demonstrates that LLMs can significantly accelerate the process of medication information extraction, achieving baseline accuracy comparable to that ...

Dec 10, 2023 · LLMs Accelerate Annotation for Medical Information Extraction ... LLMs Accelerate Annotation for Medical Information Extraction.

Extracting patient information from unstructured text is a critical task in health decision-support and clinical research. Large language models (LLMs) have ...

Dec 4, 2023 · The paper presents a method that utilizes LLMs with human expertise to speed up the annotation of medical texts while ensuring accuracy. It ...

Llms accelerate annotation for medical information extraction. A Goel, A Gueta, O Gilon, C Liu, S Erell, LH Nguyen, X Hao, B Jaber, ... Machine Learning for ...

Dec 21, 2023 · In this research we investigated the weight space, and showed that fine-tuned models reside in specific regions. We also found that we can reach ...

LLMs Accelerate Annotation for Medical Information Extraction ... The results highlight the potential of using LLMs to improve the utilization of ...

Jun 14, 2024 · Llms accelerate annotation for medical infor-. 666 mation extraction. In Machine Learning for Health. 667. (ML4H), pages 82–100. PMLR. 668.