一个可扩展和透明的数据管道,用于支持人工智能的健康数据生态系统
Tuncay Namli1, Ali Anıl Sınacı1, Suat Gönül1
1SRDC Software Research Development and Consultancy A. Ş., Ankara, Turkey.
Frontiers in medicine
|August 14, 2024
概括
我们为人工智能 (AI) 开发了一个正式的数据准备管道,以提高AI模型开发的透明度和可追溯性. 该系统确保可靠的AI应用程序从电子健康记录中可靠地提取数据.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 人工智能数据准备过程中缺乏透明度,阻碍了可重现性,调试,偏差检测和监管合规性.
- 手动数据提取过程容易出现错误,并且在开发可靠的人工智能系统方面效率低下.
- 值得信赖的AI需要强大的数据准备和可追溯性的方法.
研究的目的:
- 为人工智能应用引入正式的数据准备管道规范.
- 提高AI数据提取过程中的透明度和可追溯性.
- 从健康数据中开发可靠和可复制的人工智能模型的挑战.
主要方法:
- 开发了一种声明性语言,可以使用HL7快速医疗互操作资源 (FHIR) 配置文件从健康数据中提取AI准备的数据集.
- 创建了一个针对AI用例量身定制的共同数据模型,定义表型和AI特征规范.
- 实现了一个可扩展的功能存储库,用于执行数据准备管道,将复杂的EHR数据转换为结构化格式.
主要成果:
- 在三个研究项目中成功部署和测试了数据准备管道,包括预测心脏手术后的并发症.
- 实施的软件确保了可靠的,耐故障处理,产生AI-ready数据集与全面的元数据.
- 该框架在定义各种特征的灵活性方面表现出灵活性,在试点用例中采用特定的时间和上下文标准.
结论:
- 拟议的正式数据准备管道提高了人工智能开发的透明度和可追溯性.
- 该框架支持从复杂的健康数据中创建可靠的AI-ready数据集.
- 这种方法有助于在医疗保健和其他数据密集型领域开发可靠的人工智能系统.
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