Electronic Health Record (EHR) data frequently exhibits sparse characteristics, posing challenges for predictive modeling. Current direct imputation such as matrix imputation approaches hinge on referencing analogous rows or columns to complete raw missing data and do not differentiate between imputed and actual values. As a result, models may inadvertently incorporate irrelevant or deceptive information with respect to the prediction objective, thereby compromising the efficacy of downstream performance. While some methods strive to recalibrate or augment EHR embeddings after direct imputation, they often mistakenly prioritize imputed features. This misprioritization can introduce biases or inaccuracies into the model. To tackle these issues, our work resorts to indirect imputation, where we leverage prototype representations from similar patients to obtain a denser embedding. Recognizing the limitation that missing features are typically treated the same as present ones when measuring similar patients, our approach designs a feature confidence learner module. This module is sensitive to the missing feature status, enabling the model to better judge the reliability of each feature. Moreover, we propose a novel patient similarity metric that takes feature confidence into account, ensuring that evaluations are not based merely on potentially inaccurate imputed values. Consequently, our work captures dense prototype patient representations with feature-missing-aware calibration process. Comprehensive experiments demonstrate that designed model surpasses established EHR-focused models with a statistically significant improvement on MIMIC-III and MIMIC-IV datasets in-hospital mortality outcome prediction task. The code is publicly available at \url{https://github.com/yhzhu99/SparseEHR} to assure the reproducibility.

翻译：电子健康记录（EHR）数据经常表现出稀疏特征，这给预测建模带来了挑战。当前的直接插补方法（如矩阵插补）依赖于引用相似行或列来补全原始缺失数据，但无法区分插补值与实际值。因此，模型可能无意中引入与预测目标无关或具有误导性的信息，从而损害下游性能的有效性。尽管某些方法试图在直接插补后重新校准或增强EHR嵌入，但它们常常错误地优先处理插补特征。这种优先级错置可能给模型引入偏差或不准确性。为解决这些问题，我们的工作采用间接插补策略，利用来自相似患者的原型表征获得更密集的嵌入。鉴于在衡量相似患者时缺失特征通常与现有特征被同等对待这一局限性，我们的方法设计了一个特征置信度学习模块。该模块对缺失特征状态敏感，使模型能够更好地判断每个特征的可靠性。此外，我们提出了一种考虑特征置信度的新型患者相似度度量，确保评估不基于潜在不准确的插补值。因此，我们的工作通过特征缺失感知校准过程捕捉了密集的原型患者表征。大量实验表明，所设计的模型在MIMIC-III和MIMIC-IV数据集上院内死亡率预测任务中，以统计显著性的改进超越了现有的EHR专用模型。代码已公开发布在\url{https://github.com/yhzhu99/SparseEHR}以确保可复现性。