从EHR到机器学习:基于JSON-LD的摄入管道的初步报告
Giulia Lucrezia Baroni1, Vincenzo Della Mea1, Gian Luca Foresti1
1Dept. of Mathematics, Computer Science and Physics, University of Udine, Italy.
Studies in health technology and informatics
|August 23, 2024
概括
这项研究引入了一种用于将电子健康记录数据传输到机器学习模型的新方法. 它使用链接数据原则和JSON-LD格式进行高效的数据集成.
科学领域:
- 计算机科学 计算机科学
- 医疗信息学 医疗信息学
- 数据工程数据工程
背景情况:
- 电子健康记录 (EHR) 包含用于机器学习的宝贵数据.
- 将EHR数据集成到机器学习工作流中是具有挑战性的,因为数据的异质性和复杂性.
- 现有的方法往往缺乏标准化和互操作性.
研究的目的:
- 开发和介绍一种摄入机制的初步实验.
- 为了促进数据从电子健康记录 (EHR) 到机器学习 (ML) 过程的无传输.
- 利用链接数据概念和JSON-LD格式来改善数据集成.
主要方法:
- 对数据表示的链接数据原则的探索.
- 使用JSON-LD (链接数据的JavaScript对象符号) 格式来构建EHR数据.
- 开发一个数据传输的原型摄入机制.
- 对机制的功能进行初步实验验证.
主要成果:
- 对电子健康记录数据的功能性摄入机制的演示.
- 成功应用链接数据和JSON-LD用于数据转换.
- 从EHR到ML流程的高效数据流动的初步证据.
- 确定关键挑战和未来发展领域.
结论:
- 拟议的摄入机制对将EHR数据集成到ML中非常有希望.
- 链接数据和JSON-LD为标准化的EHR数据交换提供了可行的方法.
- 需要进一步的研究和开发来优化生产环境的机制.
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