自动ML死亡率预测算法的易感性,以模拟COVID大流行引起的模型漂移
Simone Maria Kagerbauer1,2, Bernhard Ulm3,4, Armin Horst Podtschaske3
1Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany. simone.kagerbauer@uni-ulm.de.
BMC medical informatics and decision making
|February 2, 2024
概括
机器学习 (ML) 模型在COVID大流行期间因数据漂移而退化. 简单的训练修改并不能防止性能损失,这突显了对ML模型的持续监测的需要.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习应用 机器学习应用
- 流行病学 流行病学
背景情况:
- 概念漂移和共变量转移降低了机器学习 (ML) 模型的性能.
- 由于COVID-19大流行导致数据突然漂移,因此需要对其对ML模型的影响进行调查.
- 评估ML模型对诸如流行病等外部事件的适应性,对于可靠的预测至关重要.
结论:
- 简单,易于实施的培训修改不足以防止ML模型在突然的外部事件 (如流行病) 中恶化.
- 当概念漂移和共变量转移存在时,对ML模型预测的持续监测和批判性审查至关重要.
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