Due to the limited information about emerging diseases, symptoms are hard to be noticed and recognized, so that the window for clinical intervention could be ignored. An effective prognostic model is expected to assist doctors in making right diagnosis and designing personalized treatment plan, so to promptly prevent unfavorable outcomes. However, in the early stage of a disease, limited data collection and clinical experiences, plus the concern out of privacy and ethics, may result in restricted data availability for reference, to the extent that even data labels are difficult to mark correctly. In addition, Electronic Medical Record (EMR) data of different diseases or of different sources of the same disease can prove to be having serious cross-dataset feature misalignment problems, greatly mutilating the efficiency of deep learning models. This article introduces a domain-invariant representation learning method to build a transition model from source dataset to target dataset. By way of constraining the distribution shift of features generated in disparate domains, domain-invariant features that are exclusively relative to downstream tasks are captured, so to cultivate a unified domain-invariant encoder across various task domains to achieve better feature representation. Experimental results of several target tasks demonstrate that our proposed model outperforms competing baseline methods and has higher rate of training convergence, especially in dealing with limited data amount. A multitude of experiences have proven the efficacy of our method to provide more accurate predictions concerning newly emergent pandemics and other diseases.

翻译：由于新发疾病的信息有限，其症状难以被察觉和识别，从而可能导致临床干预窗口期被忽视。有效的预后模型有望辅助医生做出正确诊断并制定个性化治疗方案，从而及时预防不良结果。然而，在疾病早期阶段，有限的数据收集和临床经验，加上隐私和伦理方面的顾虑，可能导致参考数据可用性受限，甚至难以正确标注数据标签。此外，不同疾病或同种疾病不同来源的电子病历数据之间存在严重的跨数据集特征错配问题，严重削弱了深度学习模型的效率。本文提出了一种领域不变表示学习方法，用于构建从源数据集到目标数据集的转换模型。通过约束不同领域生成特征的分布偏移，捕捉仅与下游任务相关的领域不变特征，从而在各个任务领域训练统一的领域不变编码器，以获得更优的特征表示。多个目标任务的实验结果表明，我们的模型在性能上优于现有基线方法，且训练收敛速度更快，尤其在处理有限数据量时表现突出。大量实验证明了我们的方法在针对新发流行病及其他疾病预测方面具有更高准确性。