用大模型自动构建临床试验本体，省时省钱还高效
Clinical Trials Ontology Engineering with Large Language Models
- 用GPT3.5、GPT4和Llama3从文献中提取并整合临床试验数据
- 大模型生成的本体在时间、成本上优于人工，质量接近人类水平
- 适合医疗研究机构快速获取最新临床数据
管理临床试验信息目前是医疗行业的重大挑战，传统方法耗时且成本高昂。本文提出一种简单而有效的方法，以低成本、高效率的方式提取和整合临床试验数据，帮助医疗行业及时掌握医学进展。对比了人类、GPT3.5、GPT4以及Llama3（8b与70b版本）所构建的本体在时间、成本和质量方面的表现。结果表明，大型语言模型（LLM）在自动化该过程方面具有可行性，无论从成本还是时间角度均具备优势。本研究对医学研究具有重要意义，预示着临床试验实时数据整合可能成为常态。
原文摘要 · Abstract (English)
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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