Sepsis Trajectory Prediction Using Privileged Information and Continuous Physiological Signals.

Olivia P Alge, Jonathan Gryak, J Scott VanEpps, Kayvan Najarian

Open access · goldAbstract read

In one paragraph

Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact, top 99% of its field

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Olivia P AlgeDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.ORCID 0000-0002-1029-6664

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

J Scott VanEppsMichigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI 48109, USA.ORCID 0000-0002-0805-0913

Kayvan NajarianDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.

University of Michigan · USQueens College, CUNY · US

Funding

National Science Foundation 1837985

6 · The paper itself

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.

Indexed as

machine learningprivileged informationsignal processing

Identifiers

PMID38337750

DOI10.3390/diagnostics14030234

PMCPMC10854680

OpenAlexW4391138041

What Socratic holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.