Predicting Clinical Visits Using Recurrent Neural Networks and Demographic Information
Wei Wei Wang, Hui Li, Lizhen Cui, Xiaoguang Hong, Zhongmin Yan · 2018
Early detection of diseases is a tricky problem for doctors due to the concealment of the disease itself and the patient's negligence. Electronic health records (EHRs) contain a wealth of patient information that can be used to assist in the early detection of diseases. The EHRs have the characteristics of high dimensionality and timing. The traditional non-sequential method using EHRs is not accurate for the prediction of disease and the calculation efficiency is relatively low. We use previous medical records of patients based on recurrent neural networks (RNNs) to present a prediction model named “RNN-INFO” for multiple diseases. The model can predict the probability and diagnosis of clinical visits simultaneously in a time window. The model reduces the dimension of the data through the hidden layer of the neural network and extracts the hidden representation between the medical records. In addition, in order to further improve the performance of the model, we introduce demographic information about patients. After data preprocessing, the final dataset of experiment we used contains more than 28 thousand patients and 289 thousand clinical visits. Our experiments are finished with the final dataset. The experimental results show that our model has better performance than non-temporal models on both probability prediction and diagnostic prediction. “RNN - INFO” gets better performance than RNN model without demographic information on probability prediction. It shows that the addition of demographic information can indeed be improved performance of probability prediction.