Analyzing the health status of patients based on Electronic Health Records (EHR) is a fundamental research problem in medical informatics. The presence of extensive missing values in EHR makes it challenging for deep neural networks to directly model the patient's health status based on EHR. Existing deep learning training protocols require the use of statistical information or imputation models to reconstruct missing values; however, the protocols inject non-realistic data into downstream EHR analysis models, significantly limiting model performance. This paper introduces Learnable Prompt as Pseudo Imputation (PAI) as a new training protocol. PAI no longer introduces any imputed data but constructs a learnable prompt to model the implicit preferences of the downstream model for missing values, resulting in a significant performance improvement for all EHR analysis models. Additionally, our experiments show that PAI exhibits higher robustness in situations of data insufficiency and high missing rates. More importantly, in a real-world application involving cross-institutional data with zero-shot evaluation, PAI demonstrates stronger model generalization capabilities for non-overlapping features.

翻译：基于电子健康记录（EHR）分析患者的健康状况是医学信息学中的基础研究问题。EHR中存在大量缺失值，使得深度神经网络难以直接基于EHR建立患者健康状况模型。现有深度学习训练协议要求使用统计信息或插补模型重构缺失值，但这些协议向下游EHR分析模型注入了非真实数据，显著限制了模型性能。本文提出可学习提示作为伪插补（PAI），作为一种新型训练协议。PAI不再引入任何插补数据，而是构建可学习提示来建模下游模型对缺失值的隐式偏好，从而使所有EHR分析模型的性能显著提升。此外，我们的实验表明，PAI在数据不足和高缺失率情境下表现出更强的鲁棒性。更重要的是，在涉及零样本评估的跨机构数据实际应用中，PAI对非重叠特征展现出更强的模型泛化能力。