Phenotyping hypotensive patients in critical care using hospital discharge summaries
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Author(s)
Dai, Yang
Lokhandwala, Sharukh
Long, William J
Mark, Roger G
Lehman, Li-Wei
Date Issued
April 2017
Journal
2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Dai, Yang, et al. “Phenotyping Hypotensive Patients in Critical Care Using Hospital Discharge Summaries.” 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), February 16-19 2017, Orlando, Florida, USA, Institute of Electrical and Electronics Engineers (IEEE), April 2017 © 2017 Institute of Electrical and Electronics Engineers (IEEE)
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Author's final manuscript
Abstract
Among critically-ill patients, hypotension represents a failure in compensatory mechanisms and may lead to organ hypoperfusion and failure. In this work, we adopt a datadriven approach for phenotype discovery and visualization of patient similarity and cohort structure in the intensive care unit (ICU). We used Hierarchical Dirichlet Process (HDP) as a non-parametric topic modeling technique to automatically learn a d-dimensional feature representation of patients that captures the latent 'topic' structure of diseases, symptoms, medications, and findings documented in hospital discharge summaries. We then used the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm to convert the d-dimensional latent structure learned from HDP into a matrix of pairwise similarities for visualizing patient similarity and cohort structure. Using discharge summaries of a large patient cohort from the MIMIC II database, we evaluated the clinical utility of the discovered topic structure in phenotyping critically-ill patients who experienced hypotensive episodes. Our results indicate that the approach is able to reveal clinically interpretable clustering structure within our cohort and may potentially provide valuable insights to better understand the association between disease phenotypes and outcomes.
MIT Department
Institute for Medical Engineering and Science
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/BHI.2017.7897290
