Department of Computer Science and Engineering, University at Buffalo, NY, USA.
College of Information Sciences and Technology, Pennsylvania State University, PA, USA.
AMIA Annu Symp Proc. 2022 Feb 21;2021:726-735. eCollection 2021.
Diagnosis prediction aims to predict the patient's future diagnosis based on their Electronic Health Records (EHRs). Most existing works adopt recurrent neural networks (RNNs) to model the sequential EHR data. However, they mainly utilize medical codes and ignore other useful information such as patients' clinical features and demographics. We proposed a new model called MDP to augment the prediction performance by integrating the multimodal clinical data. MDP learns the clinical feature representation by adjusting the weights of clinical features based on a patient's current health condition and demographics. Also, the clinical feature representation, diagnosis codes representation and the demographic embedding are integrated to perform the prediction task. Experiments on a real-world dataset demonstrate that MDP outperforms the state-of-the-art methods.
诊断预测旨在根据患者的电子健康记录 (EHR) 预测患者的未来诊断。大多数现有工作采用循环神经网络 (RNN) 对顺序 EHR 数据进行建模。然而，它们主要利用医疗代码，而忽略了其他有用的信息，如患者的临床特征和人口统计学信息。我们提出了一种名为 MDP 的新模型，通过整合多模态临床数据来提高预测性能。MDP 通过根据患者当前的健康状况和人口统计学信息调整临床特征的权重来学习临床特征表示。此外，临床特征表示、诊断代码表示和人口统计学嵌入被整合在一起执行预测任务。在真实数据集上的实验表明，MDP 优于最先进的方法。
