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基于人工智能的生成管道的开发和验证,用于从电子健康记录中自动提取临床数据:技术实施研究

Marvin N Carlisle¹, William A Pace¹, Andrew W Liu^1,2

¹Department of Urology, University of California, San Francisco, 550 16th Street, Box 1695, San Francisco, CA, 94158, United States, 1 5109126645.

JMIR bioinformatics and biotechnology

|January 6, 2026

概括

本研究介绍了UODBLLM,这是一个使用大语言模型 (LLM) 进行有效从电子健康记录中提取临床数据的自动化系统. 该系统实现了快速,经济高效的数据检索,加速了临床研究.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
临床数据管理临床数据管理

背景情况:

手动抽象非结构化临床数据是劳动密集型的,容易发生质量变化.
将大型语言模型 (LLM) 集成到用于医学数据提取的研究工作流中存在挑战.

研究的目的:

开发和整合基于LLM的系统,从电子健康记录 (EHR) 文本报告中自动提取数据.
将该系统纳入已建立的临床结果数据库进行研究.

主要方法:

实施了一个具有灵活的LLM接口的生成人工智能管道 (UODBLLM).
使用可扩展标记语言 (XML) 结构化的提示符和开放的数据库连接接口用于数据结构.
根据使用磁共振成像 (MRI) 报告的完成率,处理时间和提取精度来评估性能.

主要成果:

UODBLLM处理了1800份MRI报告,完成率为100%,平均处理时间为每份报告8.90秒.
提取成本最小,每份报告0.009美元,在多个批次中表现一致.
成功提取了16个结构化的临床元素,包括定量测量和分类评估,以JSON格式存储数据.

结论:

证明了基于LLM的系统的成功集成,以快速,经济高效地提取临床数据.
UODBLLM提供了一个可扩展和安全的解决方案,用于自动化数据提取,增强受保护的健康信息安全性.
这种方法可以显著加速临床研究,并使更大规模的数据库项目成为可能.

关键词:

在 GPT-4 中使用.人工智能大型语言模型聊天机器人聊天机器人生成型的人工智能 (GAI)癌癌是一种癌症.分析模式分析模式分析模式前列腺癌是前列腺癌.

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