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Updated: Jul 4, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Mapping ICU nursing intervention notes to clinical care classification using a large language model: an evaluation

Yeonju Kim^1,2, Jiin Kim^1,2, Yunseong Cho¹

¹College of Nursing, Yonsei University, Seoul, Republic of Korea.

|July 3, 2026

Summary

Large language models (LLMs) can effectively map intensive care unit (ICU) nursing notes to Clinical Care Classification (CCC) terms, showing feasibility for automated nursing data standardization. This approach offers potential to reduce manual terminology mapping burdens.

Keywords:

Clinical care classification Intensive care unit Large language models Nursing records Standardized nursing terminology

Related Experiment Videos

Last Updated: Jul 4, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Nursing Informatics
Artificial Intelligence in Healthcare
Clinical Data Standardization

Background:

Intensive care unit (ICU) nurses' free-text clinical notes present challenges for data standardization.
Existing methods for transforming unstructured nursing notes into quantifiable data are limited.
Large language models (LLMs) show potential for standardizing clinical documents, but their application in nursing requires further evidence.

Purpose of the Study:

To evaluate the capability of an LLM in mapping ICU nursing intervention notes to Clinical Care Classification (CCC) terminologies.
To assess the quality and accuracy of LLM-generated CCC mappings compared to expert-driven references.

Main Methods:

Utilized the Medical Information Mart for Intensive Care (MIMIC) IV database, extracting nursing intervention notes.
Developed expert-driven CCC mapping references from selected nursing notes.
Employed GPT-4o mini model with prompt engineering for LLM-driven CCC mapping.
Conducted quantitative and qualitative evaluations of LLM mappings against expert references.

Main Results:

Extracted over 9.6 million intervention notes, selecting 269 unique notes for analysis.
Established CCC mapping references with 17 care components, 31 interventions, and 26 sub-interventions.
LLM-driven mappings achieved performance comparable to expert mappings for approximately two-thirds of cases.
Identified reasoning failure as the primary source of mapping inconsistencies.

Conclusions:

Demonstrated the feasibility of using LLMs for automated mapping of ICU nursing notes to CCC terms.
LLM application can potentially alleviate the burden associated with manual nursing terminology mapping.
Future work should focus on enhancing LLM contextual understanding with diverse, data-rich datasets for improved generalizability.