技术报告:医疗研究和质量改进倡议的机器学习管道
Alexander A Huang1, Samuel Y Huang2
1Surgery, Northwestern University Feinberg School of Medicine, Chicago, USA.
Cureus
|November 7, 2023
概括
本研究介绍了使用R用于医学研究的简化机器学习 (ML) 管道. 它旨在减少医学计算技术的学习曲线,使研究人员更容易获得ML.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 机器学习 (ML) 在医学中的采用正在增长,但由于有限的整合到医学教育中,面临着的学习曲线.
- 研究人员经常认为计算技术具有很高的进入障碍,阻碍了它们在医学研究和质量改进中的应用.
研究的目的:
- 利用公开可用的数据和工具开发一个简洁,可访问的管道,以促进机器学习在医学研究中的使用.
- 减少医疗专业人员实施机器学习所需的学习时间,用于研究和质量改进计划.
主要方法:
- 在 R 中使用了 MLDataR 包来处理数据,并使用 XGBoost 来开发模型.
- 在模型可视化和解释方面使用了SHaply添加式解释 (SHAP).
- 用示例代码构建了一个六步过程,用于构建和可视化ML模型.
主要成果:
- 开发了一个简单的六步过程,用于机器学习模型开发和可视化.
- 建立了解释机器学习模型的三步方法.
- 提供示例代码来展示管道的功能.
结论:
- 开发的管道简化了医疗研究人员的机器学习实施,特别是那些没有广泛的计算背景的人.
- 对这些方法的进一步教育可以为研究人员提供替代数据分析技术,提高他们对医学文献和ML应用的理解.
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