In this article, we delve into the realm of biomedical knowledge extraction, where we explore the concept of term clustering and its significance in uncovering hidden patterns in large datasets. By leveraging a novel algorithm, ChatGPT-assisted BIRCH (CAB), we successfully clustered 35 million terms into 22 million clusters, providing valuable insights into the intricate web of biomedical relationships.
Clustering Biomedical Terms
Biomedical terminology is a vast landscape of interconnected concepts, with each term representing a distinct idea or function. However, when dealing with large datasets, these terms can become overwhelmingly complex, making it challenging to identify patterns and relationships. This is where term clustering comes into play, as it groups related terms together based on their semantic meaning, allowing us to uncover hidden connections and better understand the underlying biomedical knowledge.
The CAB Algorithm
To tackle this challenge, we propose a novel algorithm that leverages ChatGPT-generated explanations to infuse knowledge into the understanding and representation of unseen terms. The CAB algorithm combines the strengths of BIRCH (Billed Information Retrieval Constrained Hierarchical) clustering with the versatility of language models like ChatGPT. By fine-tuning the instruction-based text embeddings on a small dataset of anchor, positive, and negative candidates, we can train the model to represent the biomedical terms in a more informed manner.
Results
We applied the CAB algorithm to the BIOS terminology, resulting in 22 million clusters containing 35 million terms. Among these clusters, 18 million consist of only one term, while 563,523 contain more than five terms. The largest cluster contains 438 terms related to "Reversed-Phase HPLC technology." Single-term clusters often serve as subclasses of larger concepts, lacking synonymous terms within the ontology.
Demystifying Complex Concepts
To demystify complex biomedical concepts, we can use everyday language and engaging metaphors or analogies to explain them in a more accessible way. For instance, when discussing the relationship between "Reversed-Phase HPLC technology" and "Drug Metabolism," we might compare it to a recipe book where each drug molecule is like a unique dish with its own set of ingredients and cooking instructions. The BIRCH clusters help identify the various categories of dishes (or drugs) and their corresponding cooking techniques (or metabolic pathways).
Conclusion
In conclusion, term clustering is a powerful tool for uncovering hidden patterns in biomedical knowledge extraction. By leveraging the CAB algorithm, we can efficiently group related terms together based on their semantic meaning, providing valuable insights into the complex web of interconnected concepts. With the help of everyday language and engaging metaphors, we can demystify these concepts, making them more accessible to a wider audience. As the field of biomedical knowledge extraction continues to evolve, the importance of term clustering will only grow, enabling us to unlock new discoveries and advance our understanding of biomedicine.
