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://anonymous.4open.science/r/SparseEHR} to assure the reproducibility.

翻译：电子健康记录（EHR）数据通常呈现稀疏特征，给预测建模带来挑战。当前直接插补方法（如矩阵插补）依赖于参考相似行或列来补全原始缺失数据，但未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，从而损害下游任务性能。虽然部分方法尝试在直接插补后对EHR嵌入进行重新校准或增强，但它们往往错误地优先处理插补特征。这种优先级错置可能导致模型产生偏差或不准确性。针对这些问题，本文采用间接插补方法，利用相似患者的原型表示获取更密集的嵌入。考虑到在衡量患者相似性时缺失特征通常与存在特征被同等对待的局限性，我们设计了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性度量，确保评估不单纯依赖可能不准确的插补值。通过特征缺失感知校准过程，本文捕捉到了密集的原型患者表示。广泛实验表明，所提模型在MIMIC-III和MIMIC-IV数据集上的住院死亡率预测任务中，显著优于现有EHR专用模型。代码已公开于\url{https://anonymous.4open.science/r/SparseEHR}以确保可复现性。