Combining patient visual timelines with deep learning to predict mortality

PloS One
Anoop MayampurathMatthew Churpek

Abstract

Deep learning algorithms have achieved human-equivalent performance in image recognition. However, the majority of clinical data within electronic health records is inherently in a non-image format. Therefore, creating visual representations of clinical data could facilitate using cutting-edge deep learning models for predicting outcomes such as in-hospital mortality, while enabling clinician interpretability. The objective of this study was to develop a framework that first transforms longitudinal patient data into visual timelines and then utilizes deep learning to predict in-hospital mortality. All adult consecutive patient admissions from 2008-2016 at a tertiary care center were included in this retrospective study. Two-dimensional visual representations for each patient were created with clinical variables on one dimension and time on the other. Predictors included vital signs, laboratory results, medications, interventions, nurse examinations, and diagnostic tests collected over the first 48 hours of the hospital stay. These visual timelines were utilized by a convolutional neural network with a recurrent layer model to predict in-hospital mortality. Seventy percent of the cohort was used for model derivation and 30% for ...Continue Reading

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Citations

Jun 9, 2020·Pediatric Critical Care Medicine : a Journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies·Adam C DziornyL Charles Bailey
Sep 4, 2021·Critical Care Explorations·Matthew M ChurpekDavid E Leaf

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