Sepsis Trajectory Prediction Using Privileged Information and Continuous Physiological Signals-Reference-Cited by-同舟云学术

Sepsis Trajectory Prediction Using Privileged Information and Continuous Physiological Signals

Published:2024-01-23 Issue:3 Volume:14 Page:234
ISSN:2075-4418
Container-title:Diagnostics
language:en
Short-container-title:Diagnostics

Author:

Alge Olivia P.¹^ORCID,Gryak Jonathan²,VanEpps J. Scott³⁴⁵⁶⁷^ORCID,Najarian Kayvan¹³⁴⁷⁸⁹

Affiliation:

1. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA

2. Department of Computer Science, Queens College, The City University of New York, Flushing, NY 11367, USA

3. Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI 48109, USA

4. Department of Emergency Medicine, University of Michigan, Ann Arbor, MI 48109, USA

5. Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA

6. Macromolecular Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA

7. The Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, MI 48109, USA

8. Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA

9. Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109, USA

Abstract

The aim of this research is to apply the learning using privileged information paradigm to sepsis prognosis. We used signal processing of electrocardiogram and electronic health record data to construct support vector machines with and without privileged information to predict an increase in a given patient’s quick-Sequential Organ Failure Assessment score, using a retrospective dataset. We applied this to both a small, critically ill cohort and a broader cohort of patients in the intensive care unit. Within the smaller cohort, privileged information proved helpful in a signal-informed model, and across both cohorts, electrocardiogram data proved to be informative to creating the prediction. Although learning using privileged information did not significantly improve results in this study, it is a paradigm worth studying further in the context of using signal processing for sepsis prognosis.

Funder

NSF

Publisher

MDPI AG

Link

https://www.mdpi.com/2075-4418/14/3/234/pdf

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