IGNITE：时序电子健康记录中插补值的个体化生成
IGNITE: Individualized GeNeration of Imputations in Time-series Electronic health records
AI总结：
针对纵向电子健康记录缺失率高且缺失模式含个体健康信息的问题，提出带双阶段注意力和个体化缺失掩码的条件双变分自编码器模型IGNITE，可实现个性化缺失值插补与数据合成，在三个公开数据集上性能优于现有最优方法。
摘要：
电子健康记录（EHR）是推动个性化医疗的宝贵数据模态，个性化医疗会根据个体层面的差异定制治疗方案。为此，许多数据驱动的机器学习和统计模型依托丰富的纵向EHR数据，研究患者的生理状态和治疗效果。然而，纵向EHR往往数据稀疏、缺失率高，且缺失情况本身可能具备信息价值，能够反映患者潜在的健康状况。因此，面向个性化医疗的数据驱动模型能否成功，很大程度上取决于如何表征EHR中的生理数据、治疗信息以及数据中的缺失值。\n针对这一问题，我们提出了一种新型深度学习模型，该模型能够学习多变量数据随时间变化的患者潜在动态，结合个体的人口统计学特征和治疗情况，生成符合个体情况的真实合理值。我们提出的模型名为IGNITE（时序电子健康记录中插补值的个体化生成），采用增强了双阶段注意力机制的条件双变分自编码器，为个体生成缺失值。在IGNITE中，我们还提出了一种新型个体化缺失掩码（IMM），帮助模型基于个体的观测数据和缺失模式生成对应值。\n我们进一步拓展了IGNITE的应用场景，从缺失值插补延伸到个性化数据合成：它既可以生成此前从未被观测到的缺失EHR数据，甚至还能生成全新的虚拟患者数据，适配各类应用需求。我们在三个大型公开数据集上验证了模型性能，结果表明，IGNITE在缺失数据重构和任务预测两项任务上的表现均优于现有最优方法。
英文摘要：
Electronic Health Records present a valuable modality for driving personalized medicine, where treatment is tailored to fit individual-level differences. For this purpose, many data-driven machine learning and statistical models rely on the wealth of longitudinal EHRs to study patients' physiological and treatment effects. However, longitudinal EHRs tend to be sparse and highly missing, where missingness could also be informative and reflect the underlying patient's health status. Therefore, the success of data-driven models for personalized medicine highly depends on how the EHR data is represented from physiological data, treatments, and the missing values in the data. To this end, we propose a novel deep-learning model that learns the underlying patient dynamics over time across multivariate data to generate personalized realistic values conditioning on an individual's demographic characteristics and treatments. Our proposed model, IGNITE (Individualized GeNeration of Imputations in Time-series Electronic health records), utilises a conditional dual-variational autoencoder augmented with dual-stage attention to generate missing values for an individual. In IGNITE, we further propose a novel individualized missingness mask (IMM), which helps our model generate values based on the individual's observed data and missingness patterns. We further extend the use of IGNITE from imputing missingness to a personalized data synthesizer, where it generates missing EHRs that were never observed prior or even generates new patients for various applications. We validate our model on three large publicly available datasets and show that IGNITE outperforms state-of-the-art approaches in missing data reconstruction and task prediction.
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。
