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

Citation:

Tang C. (first author). 12/3/2018. “A Deep Learning Approach to Handling Temporal Variation in Chronic Obstructive Pulmonary Disease Progression.” In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Pp. 502-509. Madrid, Spain: IEEE.
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Abstract:

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.
 
Illustration of all three types of clinical notes in COPD patient (Fig. 4@Tableau).
Last updated on 04/30/2020