可扩展数值嵌入用于多元时间序列：增强医疗数据表示学习
Scalable Numerical Embeddings for Multivariate Time Series: Enhancing Healthcare Data Representation Learning
AI总结：
针对多元时间序列缺失值问题，提出SCANE框架与SUMMIT模型，通过独立标记特征值绕过插补，在三个EHR数据集上超越现有方法。
摘要：
多元时间序列（MTS）数据在采样不规则且异步时，往往呈现大量缺失值。传统的MTS分析方法倾向于依赖基于时间戳的时间嵌入，这需要后续的插补处理，然而这些插补值常常与实际值存在显著偏差，从而损害预测准确性。此外，这些方法通常无法为训练集中罕见观测甚至完全缺失的值提供稳健的初始嵌入，对模型的泛化能力构成重大挑战。针对这些挑战，我们提出了可扩展数值嵌入（SCANE），一种新颖的框架，将每个特征值视为独立的标记，有效绕过了插补的需求。SCANE规范了不同特征嵌入的特性，并通过可扩展的嵌入机制增强了表示学习。将SCANE与Transformer编码器架构相结合，我们开发了可扩展数值嵌入Transformer（SUMMIT），旨在为以普遍缺失条目为特征的MTS提供精确的预测输出。我们在三个以高缺失值频率为标志的不同的电子健康记录（EHR）数据集上进行的实验验证，证实了SUMMIT在解决类似挑战的当代最先进方法中的优越性能。这些结果证实了SCANE和SUMMIT的有效性，强调了它们在广泛的MTS数据分析任务中的潜在适用性。
英文摘要：
Multivariate time series (MTS) data, when sampled irregularly and asynchronously, often present extensive missing values. Conventional methodologies for MTS analysis tend to rely on temporal embeddings based on timestamps that necessitate subsequent imputations, yet these imputed values frequently deviate substantially from their actual counterparts, thereby compromising prediction accuracy. Furthermore, these methods typically fail to provide robust initial embeddings for values infrequently observed or even absent within the training set, posing significant challenges to model generalizability. In response to these challenges, we propose SCAlable Numerical Embedding (SCANE), a novel framework that treats each feature value as an independent token, effectively bypassing the need for imputation. SCANE regularizes the traits of distinct feature embeddings and enhances representational learning through a scalable embedding mechanism. Coupling SCANE with the Transformer Encoder architecture, we develop the Scalable nUMerical eMbeddIng Transformer (SUMMIT), which is engineered to deliver precise predictive outputs for MTS characterized by prevalent missing entries. Our experimental validation, conducted across three disparate electronic health record (EHR) datasets marked by elevated missing value frequencies, confirms the superior performance of SUMMIT over contemporary state-of-the-art approaches addressing similar challenges. These results substantiate the efficacy of SCANE and SUMMIT, underscoring their potential applicability across a broad spectrum of MTS data analytical tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
