Improving automated deep phenotyping through large language models using retrieval-augmented generation.

ABSTRACT:

Background

Diagnosing rare genetic disorders relies on precise phenotypic and genotypic analysis, with the Human Phenotype Ontology (HPO) providing a standardized language for capturing clinical phenotypes. Rule-based HPO extraction tools use concept recognition to automatically identify phenotypes, but they often struggle with incomplete phenotype assignment, requiring significant manual review. While large language models (LLMs) hold promise for more context-driven phenotype extraction, they are prone to errors and "hallucinations," making them less reliable without further refinement. We present RAG-HPO, a Python-based tool that leverages retrieval-augmented generation (RAG) to elevate accuracy of HPO term assignment by LLM. This approach bypasses the limitations of baseline mod

SUBMITTER: Garcia BT 

PROVIDER: S-EPMC12359922 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature