A Novel Deep Learning Pipeline to Analyze Temporal Clinical Data

Analysis of clinical temporal data can be difficult due to natural properties that often characterize it. The large number of variables, missing values, and other characteristics lead to issues of sparsity and high dimensional complexity. We hypothesized that a pipeline application implementing relevant deep learning methods could sequentially address these difficulties, demonstrating their combined utility in a classification task. We implemented Word2Vec, t-distributed stochastic neighbor embedding, and a convolutional neural network in a pipeline application. To test the pipeline, we applied it to a simple, binary classification task to identify patient encounter care setting. In preliminary testing, the pipeline application achieved 92% accuracy. It also produced temporal data cubes indicative of clinical encounters in intensive care unit (ICU) and non-ICU care settings. A deep learning pipeline process combining multiple methods holds promise in improving analytical tasks of clinical temporal data.