使用不平衡数据集构建基于机器学习的临床预测模型的实用指南.
Jacklyn Luu1, Evgenia Borisenko1, Valerie Przekop1
1Stanford University, Stanford, California, USA.
Trauma surgery & acute care open
|June 17, 2024
概括
本指南解释了如何使用不平衡的数据集构建用于罕见事件的强大的临床预测模型. 它涵盖了外科医生和数据科学家的基本原则和实用技术,以改善模型开发和评估.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 手术研究的研究.
背景情况:
- 临床预测模型对于识别罕见,高风险事件至关重要.
- 由于数据集不平衡,开发这些模型具有挑战性.
研究的目的:
- 为不平衡数据集建立强大的临床预测模型提供实用指南.
- 协助外科医生,数据科学家和研究人员开发和评估这些模型.
主要方法:
- 讨论了预测模型开发的基本原则.
- 亮点包括工程,算法选择和模型评估策略.
- 包括一个临床示例和代码来说明关键的考虑和陷.
主要成果:
- 强调了对不平衡数据集的特定设计技术的重要性.
- 通过编码的临床示例来展示实际应用.
- 确定了用于罕见事件的机器学习模型开发的常见陷.
结论:
- 本指南有助于开发和批判性评估可靠的临床预测模型.
- 它旨在增强外科社区在利用机器学习进行预测方面的能力.
- 改进的模型开发可以为高风险事件带来更好的患者结果.
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