Mapping Patient Trajectories using Longitudinal Extraction and Deep Learning in the MIMIC-III Critical Care Database

• Published in: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Vol. 23; p. 123
• Main Authors: Beaulieu-Jones, Brett K, Orzechowski, Patryk, Moore, Jason H
• Format: Journal Article
• Language: English
• Published: United States 2018
• ISSN: 2335-6936
• DOI: 10.1142/9789813235533_0012

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.