Knowledge graph construction for heart failure using large language models with prompt engineering

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-07-02 DOI:10.3389/fncom.2024.1389475
Tianhan Xu, Yixun Gu, Mantian Xue, Renjie Gu, Bin Li, Xiang Gu
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Abstract

IntroductionConstructing an accurate and comprehensive knowledge graph of specific diseases is critical for practical clinical disease diagnosis and treatment, reasoning and decision support, rehabilitation, and health management. For knowledge graph construction tasks (such as named entity recognition, relation extraction), classical BERT-based methods require a large amount of training data to ensure model performance. However, real-world medical annotation data, especially disease-specific annotation samples, are very limited. In addition, existing models do not perform well in recognizing out-of-distribution entities and relations that are not seen in the training phase.MethodIn this study, we present a novel and practical pipeline for constructing a heart failure knowledge graph using large language models and medical expert refinement. We apply prompt engineering to the three phases of schema design: schema design, information extraction, and knowledge completion. The best performance is achieved by designing task-specific prompt templates combined with the TwoStepChat approach.ResultsExperiments on two datasets show that the TwoStepChat method outperforms the Vanillia prompt and outperforms the fine-tuned BERT-based baselines. Moreover, our method saves 65% of the time compared to manual annotation and is better suited to extract the out-of-distribution information in the real world.
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利用大型语言模型和提示工程构建治疗心力衰竭的知识图谱
引言构建准确而全面的特定疾病知识图谱对于实际的临床疾病诊断和治疗、推理和决策支持、康复和健康管理至关重要。对于知识图谱构建任务(如命名实体识别、关系提取),基于 BERT 的经典方法需要大量训练数据来确保模型性能。然而,现实世界中的医学注释数据,尤其是特定疾病的注释样本非常有限。方法在本研究中,我们提出了一个新颖实用的管道,利用大型语言模型和医学专家提炼来构建心衰知识图谱。我们在图式设计的三个阶段应用了提示工程:图式设计、信息提取和知识完成。结果在两个数据集上的实验表明,TwoStepChat 方法优于 Vanillia 提示方法,也优于基于 BERT 的微调基线。此外,与人工标注相比,我们的方法节省了 65% 的时间,更适合在现实世界中提取分布外信息。
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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