  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.
