Linking (aligning) biomedical concepts across diverse data sources enables various integrative analyses, but it is challenging due to the discrepancies in concept naming conventions. Various strategies have been developed to overcome this challenge, such as those based on string-matching rules, manually crafted thesauri, and machine learning models. However, these methods are constrained by limited prior biomedical knowledge and can hardly generalize beyond the limited amounts of rules, thesauri, or training samples. Recently, large language models (LLMs) have exhibited impressive results in diverse biomedical NLP tasks due to their unprecedentedly rich prior knowledge and strong zero-shot prediction abilities. However, LLMs suffer from issues including high costs, limited context length, and unreliable predictions. In this research, we propose PromptLink, a novel biomedical concept linking framework that leverages LLMs. It first employs a biomedical-specialized pre-trained language model to generate candidate concepts that can fit in the LLM context windows. Then it utilizes an LLM to link concepts through two-stage prompts, where the first-stage prompt aims to elicit the biomedical prior knowledge from the LLM for the concept linking task and the second-stage prompt enforces the LLM to reflect on its own predictions to further enhance their reliability. Empirical results on the concept linking task between two EHR datasets and an external biomedical KG demonstrate the effectiveness of PromptLink. Furthermore, PromptLink is a generic framework without reliance on additional prior knowledge, context, or training data, making it well-suited for concept linking across various types of data sources. The source code is available at https://github.com/constantjxyz/PromptLink.

翻译：链接（对齐）来自不同数据源的生物医学概念能够支持多种整合分析，但由于概念命名规范的差异，这一任务具有挑战性。目前已开发出多种策略来应对这一挑战，例如基于字符串匹配规则、人工构建的同义词表以及机器学习模型的方法。然而，这些方法受限于有限的先验生物医学知识，且难以泛化到有限的规则、同义词表或训练样本之外。近年来，大型语言模型（LLMs）凭借其前所未有的丰富先验知识和强大的零样本预测能力，在多种生物医学NLP任务中展现出令人瞩目的成果。然而，LLMs也面临成本高昂、上下文长度受限及预测不可靠等问题。在本研究中，我们提出PromptLink——一种利用LLM的新型生物医学概念链接框架。该框架首先采用生物医学专属的预训练语言模型生成可适配LLM上下文窗口的候选概念，随后通过两阶段提示引导LLM进行概念链接：第一阶段提示旨在从LLM中激发用于概念链接任务的生物医学先验知识，第二阶段提示则强制LLM反思自身预测以进一步提升可靠性。在两个电子健康记录数据集与外部生物医学知识图谱间的概念链接任务上的实证结果证明了PromptLink的有效性。此外，PromptLink是一个通用框架，不依赖额外的先验知识、上下文或训练数据，因此非常适合各类数据源间的概念链接。源代码已发布在 https://github.com/constantjxyz/PromptLink。