The Application Mode of Multi-Dimensional Time Series Data Based on a Multi-Stage Neural Network

Abstract

How to use multi-dimensional time series data is a huge challenge for big data analysis. Multiple trajectories of medical use in electronic medical data are typical time series data. Although many artificial-intelligence techniques have been proposed to use the multiple trajectories of medical use in predicting the risk of concurrent medical use, most existing methods pay less attention to the temporal property of medical-use trajectory and the potential correlation between the different trajectories of medical use, resulting in limited concurrent multi-trajectory applications. To address the problem, we proposed a multi-stage neural network-based application mode of multi-dimensional time series data for feature learning of high-dimensional electronic medical data in adverse event prediction. We designed a synthetic factor for the multiple -trajectories of medical use with the combination of a Long Short Term Memory–Deep Auto Encoder neural network and bisecting k-means clustering method. Then, we used a deep neural network to produce two kinds of feature vectors for risk prediction and risk-related factor analysis, respectively. We conducted extensive experiments on a real-world dataset. The results showed that our proposed method increased the accuracy by 5%~10%, and reduced the false rate by 3%~5% in the risk prediction of concurrent medical use. Our proposed method contributes not only to clinical research, where it helps clinicians make effective decisions and establish appropriate therapy programs, but also to the application optimization of multi-dimensional time series data for big data analysis.