Mapping patient trajectories using longitudinal extraction and deep learning in the MIMIC-III Critical Care Database

Brett K. Beaulieu-Jones1, Patryk Orzechowski1,2, Jason H. Moore1


1Computational Genetics Lab, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania
2Department of Automatics and Biomedical Engineering, AGH University of Science and Technology
Email: brettbe@med.upenn.edu

Pacific Symposium on Biocomputing 23:123-132(2018)

© 2018 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.


Abstract

Electronic Health Records (EHRs) contain a wealth of patient data useful to biomedical researchers. At present, both the extraction of data and methods for analyses are frequently designed to work with a single snapshot of a patient's record. Health care providers often perform and record actions in small batches over time. By extracting these care events, a sequence can be formed providing a trajectory for a patient's interactions with the health care system. These care events also offer a basic heuristic for the level of attention a patient receives from health care providers. We show that is possible to learn meaningful embeddings from these care events using two deep learning techniques, unsupervised autoencoders and long short-term memory networks. We compare these methods to traditional machine learning methods which require a point in time snapshot to be extracted from an EHR.


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