基于混合深度学习算法的疾病预测模型

Disease Prediction Models Based on Hybrid Deep Learning Strategy

利用电子健康档案中时间序列数据建立的预测模型在改善疾病管理方面发挥着重要作用。由于时态数据的序列相关性和特征空间维度大等特点,机器学习和非深度神经网络等传统方法难以提供疾病的准确预测。最新工作表明,长短时记忆(long short term memory, LSTM)神经网络性能优于大多数传统的疾病预测方法。为了进一步提高预测精度,本文提出了一种将卷积神经网络(convolutional neural network, CNN)与LSTM相结合的混合深度学习神经网络框架。使用电子健康档案中真实数据集的研究结果表明,相比传统SVM,CNN和LSTM模型,该算法的预测性能得到显著提高。

Predictive models built using temporal data in electronic health records (EHRs) can potentially play a major role in improving management of diseases. Due to the sequence correlation and large feature space dimensions, traditional methods such as machine learning and non-deep neural networks are difficult to provide accurate predictions of disease. Recent works show that the long short term memory (LSTM) neural network outperforms most of those traditional methods for disease prediction problems. In this study, a hybrid deep learning neural network framework that combines convolutional neural network (CNN) with LSTM is proposed to further improve the pre-diction accuracy. Empirical studies using the real-world datasets in electronic health records have shown that using the proposed hybrid deep learning neural network for disease prediction signif-icantly improves predictive performance compared to the use of support vector machine (SVM) model, CNN and LSTM alone.