Medical Examination Data Prediction Using Simple Recurrent Network and Long Short-Term Memory

Abstract

In this work, we use two different types of recurrent neural networks (RNNs) to predict medical examination results of a subject given the previous measurements. The first one is a simple recurrent network (SRN) which models temporal trajectories of a data sequence to infer the unknown future observation, and the second one is a long short-term memory (LSTM) that enables modeling the longer trajectories by exploiting forgetting switches. The non-linear, temporal evolution of medical status of a human subjects are approx-imated by the RNNs, and the prediction of the future measurement becomes more accurate than those of the linear approximation method. The performance evaluation experiments are carried out on the real medical examination data, and the proposed methods show superior performances over the linear regression method. For the subjects who have abnormal behaviors in their medical examination results, the performance improvements are much more significant, so the proposed methods are expected to be used in detecting potential patients to provide earlier diagnosis and proper treatments for their illnesses.