Mei Jing, Zhao Shiwan, Jin Feng, Zhang Lingxiao, Liu Haifeng, Li Xiang, Xie Guotong, Li Xuejun, Xu Meilin
IBM Research China, Beijing, China.
Department of Computer Science, Peking University, Beijing, China.
Stud Health Technol Inform. 2017;245:1277.
In healthcare, applying deep learning models to electronic health records (EHRs) has drawn considerable attention. This sequential nature of EHR data make them wellmatched for the power of Recurrent Neural Network (RNN). In this poster, we propose "Deep Diabetologist" - using RNNs for EHR sequential data modeling to provide personalized hypoglycemic medication prediction for diabetic patients. Our results demonstrate improved performance compared with a baseline classifier using logistic regression.
在医疗保健领域,将深度学习模型应用于电子健康记录(EHRs)已引起了广泛关注。EHR数据的这种序列性质使其非常适合循环神经网络(RNN)的强大功能。在本海报中,我们提出了“深度糖尿病专家”——使用RNN对EHR序列数据进行建模,为糖尿病患者提供个性化的降血糖药物预测。我们的结果表明,与使用逻辑回归的基线分类器相比,性能有所提高。