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

Pac Symp Biocomput. 2018;23:123-132.

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Computational Biology / methods
  • Critical Care / statistics & numerical data*
  • Databases, Factual / statistics & numerical data
  • Electronic Health Records / statistics & numerical data
  • Female
  • Humans
  • Machine Learning / statistics & numerical data*
  • Male
  • Supervised Machine Learning / statistics & numerical data
  • Unsupervised Machine Learning / statistics & numerical data