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A study of Generalizability of Recurrent Neural Network-Based Predictive Models for Heart Failure Onset Risk using a Large and Heterogeneous EHR Data set

机译：基于大型和异构EHR数据集的基于递归神经网络的心衰发作风险预测模型的可推广性研究

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Recently, recurrent neural networks (RNNs) have been applied in predicting disease onset risks with Electronic Health Record (EHR) data. While these models demonstrated promising results on relatively small data sets, the generalizability and transferability of those models and its applicability to different patient populations across hospitals have not been evaluated. In this study, we evaluated an RNN model, RETAIN, over Cerner Health Facts® EMR data, for heart failure onset risk prediction. Our data set included over 150,000 heart failure patients and over 1,000,000 controls from nearly 400 hospitals. Convincingly, RETAIN achieved an AUC of 82% in comparison to an AUC of 79% for logistic regression, demonstrating the power of more expressive deep learning models for EHR predictive modeling. The prediction performance fluctuated across different patient groups and varied from hospital to hospital. Also, we trained RETAIN models on individual hospitals and found that the model can be applied to other hospitals with only about 3.6% of reduction of AUC. Our results demonstrated the capability of RNN for predictive modeling with large and heterogeneous EHR data, and pave the road for future improvements.
机译：最近，递归神经网络（RNN）已用于通过电子健康记录（EHR）数据预测疾病发作的风险。尽管这些模型在相对较小的数据集上显示出令人鼓舞的结果，但尚未评估这些模型的可推广性和可移植性及其在医院中不同患者群体的适用性。在这项研究中，我们根据Cerner HealthFacts®EMR数据评估了RNN模型RETAIN，以预测心衰发作风险。我们的数据集包括来自将近400家医院的150,000多名心力衰竭患者和1,000,000多名对照者。令人信服的是，RETAIN实现了82％的AUC，而Logistic回归的AUC为79％，证明了更具表现力的深度学习模型在EHR预测建模中的强大功能。预测性能在不同患者组之间波动，并且因医院而异。此外，我们在各个医院对RETAIN模型进行了训练，发现该模型仅可将AUC减少约3.6％即可应用于其他医院。我们的结果证明了RNN具有使用大型且异构EHR数据进行预测建模的能力，并为未来的改进铺平了道路。

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