从改变临床特征的深度持续多任务医院外事件严重性评估
Pablo Ferri1, Carlos Sáez1, Antonio Félix-De Castro2
1Biomedical Data Science Laboratory (BDSLab), Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València (UPV), Camí de Vera s/n, 46022 València, Spain.
Health information science and systems
|November 21, 2025
概括
深度持续学习管道通过适应不断变化的输入特征来增强紧急医疗分拣的机器学习模型. 新的策略提高了识别关键情况和预测响应时间的性能.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 紧急医疗 紧急医疗
背景情况:
- 机器学习模型在紧急医疗分拣中容易受到性能下降的影响,因为随着时间的推移,输入特征的演变.
- 共同变量转移,包括特征的出现和消失,对模型准确性和可靠性构成重大挑战.
研究的目的:
- 评估新的深度持续学习管道,以维持和改善动态紧急医疗分拣场景中的模型性能.
- 为了应对随时间变化的数据集中变化的功能域和参数更新所带来的挑战.
主要方法:
- 分析了2009-2019年间的1,414,575个紧急事件,以识别和量化共变量转移.
- 开发和评估持续学习策略,包括静态/动态领域方法和参数更新方法 (从头开始,微调,累积,排练,弹性重量巩固).
主要成果:
- 动态特征域和弹性重量巩固 (EWC) 的最佳组合在危及生命的情况中提高了F1得分,高达5.9%,响应延迟18.6%,管辖权2.4%.
- 持续学习方法的表现优于非持续方法,在关键情况下表现出高达5.4%的收益,在响应延迟方面表现出11%的收益.
结论:
- 提出的持续学习方法有效地减轻了在紧急医疗分组中共变量轮班的负面影响.
- 这些进步提高了机器学习模型在现实世界的可靠性和准确性,不断发展的紧急医疗系统.
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