在临床预测建模中比较机器学习和后勤回归之外:从模型辩论转向数据质量
Yanan Hu1, Xin Zhang2, Valerie Slavin3,4,5,6
1Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
Journal of medical Internet research
|November 5, 2025
概括
对于具有表格数据的临床预测模型,机器学习 (ML) 对逻辑回归没有普遍的优势. 提高数据质量,而不是模型复杂性,是提高模型可靠性和现实世界的实用性的关键.
科学领域:
- 临床预测建模临床预测建模
- 机器学习在医疗保健中的应用
- 统计建模 统计建模
背景情况:
- 监督机器学习 (ML) 越来越多地用于临床预测,特别是表格数据的二进制结果.
- 在这个领域,关于ML在传统物流回归方面的优势存在争议.
- 在非结构化数据中,ML优于非结构化数据,但在结构化临床数据中的性能增长是不一致的.
研究的目的:
- 对临床预测中的ML与物流回归进行比较研究和模拟发现进行综合.
- 论证一种普遍优越的建模方法.
- 确定影响模型性能的因素,并建议改进的途径.
主要方法:
- 最近比较研究的文献综合.
- 对模拟结果的审查.
- 基于综合证据的观点论证.
主要成果:
- 没有一种单一的建模方法 (ML或逻辑回归) 是使用表格数据进行临床预测的普遍最佳方法.
- 模型性能高度依赖于数据集特征 (例如线性,样本大小,预测器数量,类比例) 和数据质量 (完整性,准确性).
结论:
- 努力应专注于提高数据质量,而不是增加模型复杂性.
- 提高数据质量更有可能提高临床预测模型的可靠性和现实世界的实用性.
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