@inproceedings{guo-etal-2025-lab,
title = "{LAB}-{KG}: A Retrieval-Augmented Generation Method with Knowledge Graphs for Medical Lab Test Interpretation",
author = "Guo, Rui and
Devereux, Barry and
Farnan, Greg and
McLaughlin, Niall",
editor = "Liu, Kang and
Song, Yangqiu and
Han, Zhen and
Sifa, Rafet and
He, Shizhu and
Long, Yunfei",
booktitle = "Proceedings of Bridging Neurons and Symbols for Natural Language Processing and Knowledge Graphs Reasoning @ COLING 2025",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2025.neusymbridge-1.5/",
pages = "40--50",
abstract = "Laboratory tests generate structured numerical data, which a clinician must interpret to justify diagnoses and help patients understand the outcomes of the tests. LLMs have the potential to assist with the generation of interpretative comments, but legitimate concerns remain about the accuracy and reliability of the generation process. This work introduces LAB-KG, which conditions the generation process of an LLM on information retrieved from a knowledge graph of relevant patient conditions and lab test results. This helps to ground the text-generation process in accurate medical knowledge and enables generated text to be traced back to the knowledge graph. Given a dataset of laboratory test results and associated interpretive comments, we show how an LLM can build a KG of the relationships between laboratory test results, reference ranges, patient conditions and demographic information. We further show that the interpretive comments produced by an LLM conditioned on information retrieved from the KG are of higher quality than those from a standard RAG method. Finally, we show how our KG approach can improve the interpretability of the LLM generated text."
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%0 Conference Proceedings
%T LAB-KG: A Retrieval-Augmented Generation Method with Knowledge Graphs for Medical Lab Test Interpretation
%A Guo, Rui
%A Devereux, Barry
%A Farnan, Greg
%A McLaughlin, Niall
%Y Liu, Kang
%Y Song, Yangqiu
%Y Han, Zhen
%Y Sifa, Rafet
%Y He, Shizhu
%Y Long, Yunfei
%S Proceedings of Bridging Neurons and Symbols for Natural Language Processing and Knowledge Graphs Reasoning @ COLING 2025
%D 2025
%8 January
%I ELRA and ICCL
%C Abu Dhabi, UAE
%F guo-etal-2025-lab
%X Laboratory tests generate structured numerical data, which a clinician must interpret to justify diagnoses and help patients understand the outcomes of the tests. LLMs have the potential to assist with the generation of interpretative comments, but legitimate concerns remain about the accuracy and reliability of the generation process. This work introduces LAB-KG, which conditions the generation process of an LLM on information retrieved from a knowledge graph of relevant patient conditions and lab test results. This helps to ground the text-generation process in accurate medical knowledge and enables generated text to be traced back to the knowledge graph. Given a dataset of laboratory test results and associated interpretive comments, we show how an LLM can build a KG of the relationships between laboratory test results, reference ranges, patient conditions and demographic information. We further show that the interpretive comments produced by an LLM conditioned on information retrieved from the KG are of higher quality than those from a standard RAG method. Finally, we show how our KG approach can improve the interpretability of the LLM generated text.
%U https://aclanthology.org/2025.neusymbridge-1.5/
%P 40-50
Markdown (Informal)
[LAB-KG: A Retrieval-Augmented Generation Method with Knowledge Graphs for Medical Lab Test Interpretation](https://aclanthology.org/2025.neusymbridge-1.5/) (Guo et al., NeusymBridge 2025)
ACL