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Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...

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Updated: May 31, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Large language models for intelligent RDF knowledge graph construction: results from medical ontology mapping.

Apostolos Mavridis¹, Stergios Tegos¹, Christos Anastasiou¹

¹School of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.

Frontiers in Artificial Intelligence

|May 12, 2025

Summary

This summary is machine-generated.

Large Language Models (LLMs) automate medical ontology mapping for RDF knowledge graphs, improving accuracy. GPT-4o shows superior performance in creating these complex medical knowledge graphs.

Keywords:

LLM RDF SNOMED CT health data knowledge graph ontology

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Area of Science:

Medical Informatics
Artificial Intelligence
Knowledge Representation

Background:

Digital data growth, especially in healthcare, requires advanced knowledge representation.
Medical ontologies like SNOMED CT are complex, making RDF knowledge graph creation challenging.
Traditional methods struggle with the scale and semantic complexity of medical data.

Purpose of the Study:

To introduce a methodology using LLMs for automating medical ontology mapping.
To enhance the construction of RDF knowledge graphs for medical data.
To evaluate the performance of various LLMs in this task.

Main Methods:

Developed a data preprocessing pipeline and an LLM-powered semantic mapping engine.
Integrated BioBERT embeddings and ChromaDB for efficient concept retrieval.
Conducted a comparative analysis of six systems (GPT-4o, Claude 3.5 Sonnet v2, Gemini 1.5 Pro, Llama 3.3 70B, DeepSeek R1, BERTMap) using quantitative and qualitative metrics.

Main Results:

Modern LLMs, particularly GPT-4o, demonstrated superior performance in medical ontology mapping.
GPT-4o achieved 93.75% precision and 96.26% F1-score in experiments.
The proposed methodology effectively enhances RDF knowledge graph construction.

Conclusions:

LLMs show significant potential in automating and improving medical ontology mapping.
This approach can bridge the gap between structured medical data and semantic knowledge representation.
The findings pave the way for more accurate and interoperable medical knowledge graphs.