arXiv 2401.089402024-01-17cs.LGcs.AI

CEL：一种利用弹性权重巩固进行领域自适应以用于疾病暴发预测的持续学习模型

CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；University of Macau（澳门大学）

Saba Aslam, Abdur Rasool, Hongyan Wu, Xiaoli Li

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AI总结：

本文提出持续学习模型CEL，利用弹性权重巩固进行领域自适应以缓解灾难性遗忘，在流感、猴痘和麻疹预测中表现优于现有模型，遗忘率低且记忆稳定性高。

摘要：

持续学习是指模型能够随时间不断学习而不遗忘先前知识，从而适应新数据的能力，在疾病暴发预测等动态领域中至关重要。深度神经网络（如LSTM）容易因灾难性遗忘而产生错误。本研究提出了一种新颖的CEL模型，通过弹性权重巩固（EWC）利用领域自适应实现持续学习。该模型旨在缓解领域增量设置下的灾难性遗忘现象。利用EWC构建Fisher信息矩阵（FIM），以形成正则化项，对重要参数（即重要的先前知识）的变化进行惩罚。在三种不同疾病——流感（Influenza）、猴痘（Mpox）和麻疹（Measles）上，使用不同指标评估了CEL的性能。评估和重新评估期间的高R平方值在多个场景中优于其他最先进模型，表明CEL能够很好地适应增量数据。与现有基准研究相比，CEL最低65%的遗忘率和高出18%的记忆稳定性凸显了其鲁棒性和可靠性。本研究突出了CEL在疾病暴发预测中的多功能性，能够处理具有时间模式的不断演变的数据。它为通过准确、及时的预测进行主动疾病控制提供了一个有价值的模型。

英文摘要：

Continual learning, the ability of a model to learn over time without forgetting previous knowledge and, therefore, be adaptive to new data, is paramount in dynamic fields such as disease outbreak prediction. Deep neural networks, i.e., LSTM, are prone to error due to catastrophic forgetting. This study introduces a novel CEL model for continual learning by leveraging domain adaptation via Elastic Weight Consolidation (EWC). This model aims to mitigate the catastrophic forgetting phenomenon in a domain incremental setting. The Fisher Information Matrix (FIM) is constructed with EWC to develop a regularization term that penalizes changes to important parameters, namely, the important previous knowledge. CEL's performance is evaluated on three distinct diseases, Influenza, Mpox, and Measles, with different metrics. The high R-squared values during evaluation and reevaluation outperform the other state-of-the-art models in several contexts, indicating that CEL adapts to incremental data well. CEL's robustness and reliability are underscored by its minimal 65% forgetting rate and 18% higher memory stability compared to existing benchmark studies. This study highlights CEL's versatility in disease outbreak prediction, addressing evolving data with temporal patterns. It offers a valuable model for proactive disease control with accurate, timely predictions.

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