Motivation: Electronic Health Records (EHR) represent a comprehensive resource of a patient's medical history. EHR are essential for utilizing advanced technologies such as deep learning (DL), enabling healthcare providers to analyze extensive data, extract valuable insights, and make precise and data-driven clinical decisions. DL methods such as Recurrent Neural Networks (RNN) have been utilized to analyze EHR to model disease progression and predict diagnosis. However, these methods do not address some inherent irregularities in EHR data such as irregular time intervals between clinical visits. Furthermore, most DL models are not interpretable. In this study, we propose two interpretable DL architectures based on RNN, namely Time-Aware RNN (TA-RNN) and TA-RNN-Autoencoder (TA-RNN-AE) to predict patient's clinical outcome in EHR at next visit and multiple visits ahead, respectively. To mitigate the impact of irregular time intervals, we propose incorporating time embedding of the elapsed times between visits. For interpretability, we propose employing a dual-level attention mechanism that operates between visits and features within each visit. Results: The results of the experiments conducted on Alzheimer's Disease Neuroimaging Initiative (ADNI) and National Alzheimer's Coordinating Center (NACC) datasets indicated superior performance of proposed models for predicting Alzheimer's Disease (AD) compared to state-of-the-art and baseline approaches based on F2 and sensitivity. Additionally, TA-RNN showed superior performance on Medical Information Mart for Intensive Care (MIMIC-III) dataset for mortality prediction. In our ablation study, we observed enhanced predictive performance by incorporating time embedding and attention mechanisms. Finally, investigating attention weights helped identify influential visits and features in predictions. Availability: https://github.com/bozdaglab/TA-RNN

翻译：动机：电子健康记录（EHR）是患者病史的综合性资源。EHR对于利用深度学习等先进技术至关重要，能够使医疗提供者分析海量数据、提取有价值的信息，并做出精准且数据驱动的临床决策。诸如循环神经网络等深度学习方法已被用于分析EHR，以建模疾病进展和预测诊断。然而，这些方法未能解决EHR数据中的一些固有不规则性，例如临床就诊之间的不规则时间间隔。此外，大多数深度学习模型不具备可解释性。在本研究中，我们提出了两种基于RNN的可解释深度学习架构，即时序感知RNN（TA-RNN）和TA-RNN自编码器（TA-RNN-AE），分别用于预测患者在下一次就诊及未来多次就诊中的临床结局。为减轻不规则时间间隔的影响，我们提出引入就诊间经过时间的时间嵌入。为实现可解释性，我们提出采用一种在就诊间及每次就诊内特征间运行的双层注意力机制。结果：在阿尔茨海默病神经影像学倡议（ADNI）和国立阿尔茨海默病协调中心（NACC）数据集上进行的实验结果表明，基于F2和敏感性指标，所提出的模型在预测阿尔茨海默病方面相较于最先进方法和基线方法表现更优。此外，TA-RNN在重症监护医学信息集市（MIMIC-III）数据集上进行死亡率预测时也表现出更优性能。在我们的消融研究中，我们发现通过引入时间嵌入和注意力机制增强了预测性能。最后，探究注意力权重有助于识别预测中具有影响力的就诊和特征。可用性：https://github.com/bozdaglab/TA-RNN