从电子表格和定制模型到企业数据仓库:支持GPT的临床数据摄入到i2b2
Taowei David Wang1,2, Shawn N Murphy1,2,3, Victor M Castro2
1Harvard Medical School, Boston, MA.
概括
我们开发了一个大型语言模型 (LLM) 驱动的管道,以有效地将各种临床数据集转换为i2b2格式. 这为使用AnVIL等平台的研究人员提供了先进的分析和队列发现.
科学领域:
- 基因组数据科学 基因组数据科学
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
背景情况:
- 临床和表型数据通常以不同的格式分离,阻碍了复杂的分析和队列发现.
- 将这些数据集集成到企业数据仓库中对于利用像NHGRI的AnVIL这样的平台至关重要.
- 现有的数据集成方法可能是复杂和耗时的.
研究的目的:
- 开发一个自动化和高效的管道,将异质的临床数据转化为标准化格式.
- 为基因组数据科学分析,可视化和信息学实验室空间 (AnVIL) 的用户提供先进的分析和队列发现.
- 利用大型语言模型 (LLM) 来简化提取,转换和加载 (ETL) 过程.
主要方法:
- 使用ChatGPT-4o的几次射击提示来生成ETL的Python脚本,用于将生物和床边 (i2b2) 平台集成到信息学中.
- 开发脚本将数据字典转换为中间常用格式,然后转换为i2b2本体.
- 采用国家生物医学本体学中心 (NCBO) 的标准本体学,将数据映射成i2b2事实.
主要成果:
- 聊天GPT-4o成功生成了用于ETL过程的Python代码.
- 来自三个不同模型的合成数据集的表型数据被转换为i2b2格式.
- 成功处理了3,458名合成患者的数据,使其在i2b2.2.中可查询.
结论:
- 一个由LLM驱动的ETL管道有效地将异质数据集转换为i2b2格式.
- 这种方法可以在各种数据模型中实现高级分析和队列发现.
- 该管道可节省大量时间,并有可能改变未来的ETL工作流程.
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