A Deep Learning Approach to Handling Temporal Variation in Chronic Obstructive Pulmonary Disease Progression

摘要

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of mortality in the United States. Representing COPD progression using temporal graphs may offer critical clinical insights. Long-Short Term Memory units in recurrent neural networks can process data with constant elapsed times between consecutive elements of a sequence but cannot handle irregular time intervals (i.e., segments with unequal-time). In this study, we propose a four-layer deep learning model that utilizes a specially configured recurrent neural network to capture irregular time lapse segments. Experiments on a corpus of COPD patients' clinical notes compared to baseline algorithms showed that our model improved interpretability as well as the accuracy of estimating COPD progression.