Clinical Trials Ontology Engineering with Large Language Models

• URL: http://arxiv.org/abs/2412.14387v1
• Date: Wed, 18 Dec 2024 22:40:52 GMT
• Title: Clinical Trials Ontology Engineering with Large Language Models
• Authors: Berkan Çakır,
• Abstract summary: This paper proposes a simple yet effective methodology to extract and integrate clinical trial data in a cost-effective manner.<n>Findings suggest that large language models (LLM) are a viable option to automate this process from a cost and time perspective.<n>This study underscores significant implications for medical research where real-time data integration from clinical trials could become the norm.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Managing clinical trial information is currently a significant challenge for the medical industry, as traditional methods are both time-consuming and costly. This paper proposes a simple yet effective methodology to extract and integrate clinical trial data in a cost-effective and time-efficient manner. Allowing the medical industry to stay up-to-date with medical developments. Comparing time, cost, and quality of the ontologies created by humans, GPT3.5, GPT4, and Llama3 (8b & 70b). Findings suggest that large language models (LLM) are a viable option to automate this process both from a cost and time perspective. This study underscores significant implications for medical research where real-time data integration from clinical trials could become the norm.

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