为了实现预测模型的互操作性和可重复性,建立一个框架
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Updated: Jul 10, 2025
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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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Al Rahrooh1, Anders O Garlid1, Kelly Bartlett1
1Medical & Imaging Informatics (MII) Group, University of California Los Angeles (UCLA), Los Angeles, CA, USA.
Journal of biomedical informatics
|November 24, 2023
概括
本研究引入了一个自动化元数据管道 (AMP) 以标准化机器学习 (ML) 模型在医疗保健中的可重复性. AMP使用扩展的预测模型标记语言 (PMML) 来确保模型是可共享和可比的.
科学领域:
- 生物医学信息学是生物医学信息学.
- 机器学习在医疗保健中的应用.
- 计算生物学是一种计算生物学.
背景情况:
- 目前缺乏用于开发和部署机器学习 (ML) 模型在生物医学研究和医疗保健中的标准化方法.
- 现有的模型复制工具没有提供统一的蓝图,由于不清楚的假设,预处理步骤和测试指标,阻碍了科学可重复性.
- 机器学习模型的通用性和可移植性的挑战仍然是该领域的重要问题.
研究的目的:
- 为了促进生物医学机器学习的科学可重现性.
- 将自动化元数据管道 (AMP) 作为预测模型指数和交换存储库 (PREMIERE) 平台的关键组件.
- 为了使预测ML模型能够转换为扩展PMML格式,以提高互操作性和可重复性.
主要方法:
- 在预测模型标记语言 (PMML) 基础上开发自动化元数据管道 (AMP).
- 将预测ML模型转换为扩展的PMML文件.
- 基于ML的检查列表的自动填写,以评估模型元素的互操作性和可重复性.
- 使用三个不同的ML算法和与健康相关的数据集,在多个测试案例上展示管道.
主要成果:
- 成功地将ML模型转换为使用AMP的扩展PMML文件.
- 通过自动填写的检查清单,通过互操作性和可重复性来证明模型元素的评估.
- 通过各种ML算法和健康数据集验证管道.
结论:
- 自动化元数据管道 (AMP) 提供了一个基础解决方案,用于提高医疗保健中的预测ML模型的可重复性.
- 扩展的PMML格式有助于更好的模型共享,比较和理解可通用性和可运输性.
- 这项工作为生物医学研究和临床实践中更强大,更可靠的ML应用铺平了道路.
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