A Simplified Retriever to Improve Accuracy of Phenotype Normalizations by Large Language Models
This work addresses phenotype normalization for biomedical applications, offering an efficient alternative to complex methods, though it is incremental as it builds on existing retriever-augmented LLM approaches.
The paper tackled the problem of phenotype term normalization by introducing a simplified retriever that uses BioBERT embeddings to search the Human Phenotype Ontology, increasing LLM accuracy from 62.3% to 90.3% on OMIM clinical synopses.
Large language models (LLMs) have shown improved accuracy in phenotype term normalization tasks when augmented with retrievers that suggest candidate normalizations based on term definitions. In this work, we introduce a simplified retriever that enhances LLM accuracy by searching the Human Phenotype Ontology (HPO) for candidate matches using contextual word embeddings from BioBERT without the need for explicit term definitions. Testing this method on terms derived from the clinical synopses of Online Mendelian Inheritance in Man (OMIM), we demonstrate that the normalization accuracy of a state-of-the-art LLM increases from a baseline of 62.3% without augmentation to 90.3% with retriever augmentation. This approach is potentially generalizable to other biomedical term normalization tasks and offers an efficient alternative to more complex retrieval methods.