心脏模拟领域终于有了能应对临床数据流变化的方案——CoMetaPNS解决了灾难性遗忘问题，做个性化医疗模拟或心脏电生理研究的团队可以直接参考其持续学习框架。
个性化心脏模拟面临模型个性化和计算成本挑战，现有神经代理方法要么侧重高效个性化，要么侧重泛化模型训练。最新研究通过小样本生成建模和元学习实现个性化，但假设训练分布静态且任务标识已知，无法处理临床中顺序到达的未标记数据，否则会灾难性遗忘。本文提出持续元学习框架CoMetaPNS，利用持续贝叶斯高斯混合模型推断数据标识和关系，实现个性化神经代理的持续集成。在合成心脏数据上，该方法在模拟预测、计算可扩展性和抗遗忘方面优于现有基线。
CoMetaPNS: Continually Meta-learning Personalized Neural Surrogates for Cardiac Electrophysiology Simulations
Personalized virtual heart simulations face challenges in model personalization and computational cost. While neural surrogates offer state-of-the-art solutions, they typically address either efficient personalization or training generalizable models. Recent work reframes this by learning the process of personalizing a surrogate using limited subject-specific context data, through few-shot generative modeling with set-conditioned surrogates and meta-learned amortized inference. These methods, however, assume a static and diverse training distribution with known task identifiers. When new data becomes available, they require costly retraining with all prior data to avoid catastrophic forgetting - a phenomena where the model forgets earlier tasks when trained on new ones. This is a major limitation in clinical settings where often unlabeled data arrives sequentially and full retraining is infeasible. This paper presents a new continual meta-learning framework to achieve personalized neural surrogates able to not only continually integrate information but also identify whether incoming data stems from a known or unknown dynamics source. By leveraging a continual Bayesian Gaussian Mixture Model over a memory buffer, our framework can infer the identifiers and relationships of data over time - required for effective meta-learning. Empirical results on synthetic cardiac data demonstrate superior simulation forecasting, computational scalability, and resilience to catastrophic forgetting compared to existing baselines.