CEL:通过通过弹性重量巩固利用域适应来预测疾病爆发的持续学习模型
Saba Aslam1,2, Abdur Rasool1,2, Xiaoli Li1,3,4
1Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Interdisciplinary sciences, computational life sciences
|February 28, 2025
概括
这项研究引入了一种新的持续学习 (CEL) 模型,使用弹性重量巩固 (EWC) 来防止疾病爆发预测中的灾难性遗忘. CEL表现出强大的适应新数据,在最小的遗忘率下优于现有模型.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 流行病学 流行病学
背景情况:
- 深度神经网络经常在学习新信息时遭受灾难性的遗忘.
- 适应动态数据对于准确预测疾病爆发至关重要.
- 现有的模型在不断变化的流行病学环境中与增量学习作斗争.
研究的目的:
- 引入一种新的持续学习 (CEL) 模型,以减轻域增量设置中的灾难性遗忘.
- 通过弹性重量巩固 (EWC) 利用域调整来提高模型稳定性.
- 通过不断变化的数据,提高疾病爆发预测的准确性.
主要方法:
- 开发了一种持续学习 (CEL) 模型,将域适应与弹性重量巩固 (EWC) 整合在一起.
- 在欧洲工会内部构建了一个费舍尔信息矩阵 (FIM),以创建一个规范化术语,惩罚基本参数变化.
- 使用定制评估指标对流感,mopox和麻疹数据集进行了实验.
主要成果:
- 在评估和重新评估中,CEL模型表现出高的R平方值,超过了最先进的模型.
- 对于增量数据,CEL表现出强大的适应能力,遗忘率最低 (65%),记忆稳定性提高 (18%以上).
- 实验结果证实了CEL在处理具有时间模式的不断变化的数据方面的有效性.
结论:
- 新的CEL模型有效地解决了疾病爆发预测的持续学习中的灾难性遗忘.
- 由于CEL的稳定性和适应性,使其成为预防性疾病控制的宝贵工具.
- 这项研究为动态流行病学领域的准确和及时预测提供了一个多功能模型.
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