A novel approach for Rare Disease symptom extraction from Clinical Texts Using Knowledge Graph Embeddings and Large Language Models

Abstract

Rare diseases are medical conditions affecting a small percentage of the population, often leading to diagnostic challenges due to limited clinical data and symptom overlap with common diseases. Due to the lack of knowledge about these disorders and the fact that many of their symptoms are similar to those of common disorders, doctors find it difficult to diagnose these conditions. This paper leverages DeepSeek Plus Knowledge Graph for high-precision symptom extraction in rare diseases, integrating natural language processing (NLP) and graph-based reasoning to enhance disease recognition. This study presents a novel model called “Rarepheno” that extracts symptoms related to rare diseases from clinical text using DeepSeek Large Language Model (LLM) and knowledge graphs (KG). The proposed model integrates DeepSeek AI, N-GRAM based term extraction, and the Human Phenotype Ontology (HPO) knowledge graph to enhance symptom identification. To improve contextual precision and reduce false positives, the system leverages semantic similarity through BioClinicalBERT embeddings.  The proposed model outperforms the state-of-the-art rare disease phenotyping algorithms, with a precision score of 0.83, compared to PhenoGPT (GPT-J) (0.809), PhenoTagger (0.720), and ClinPhen (0.590). Compared to rule-based systems like MetaMap (0.707) and NCBO (0.777), Rarepheno demonstrated superior accuracy in extracting standardized symptom terms. This performance gain reflects its ability to reduce false positives and improve symptom-disease association, particularly in heterogeneous and unstructured clinical texts. This study demonstrates how the new-age technologies, such as GenAI and Natura Language Processing (NLP) can aid in collecting valuable clinical data, improving patient safety and rare disease identification.