Combining machine learning and physiological network models for sepsis prediction.

Juri Backes, Artyom Tsanda, Tobias Knopp, Wolfgang Renz, Eckehard Schöll

Abstract read

In one paragraph

Article in Frontiers in network physiology, 2026. 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

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.

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

5 authors.

Juri BackesInstitute for Biomedical Imaging, Hamburg University of Technology, Hamburg, Germany.

Artyom TsandaInstitute for Biomedical Imaging, Hamburg University of Technology, Hamburg, Germany.

Tobias KnoppInstitute for Biomedical Imaging, Hamburg University of Technology, Hamburg, Germany.

Wolfgang RenzFaculty of Electrical, Media and Information Engineering, Hamburg University of Applied Sciences (HAW Hamburg), Hamburg, Germany.

Eckehard SchöllInstitut für Physik und Astronomie, Technische Universität Berlin, Berlin, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the most extreme course of an infectious disease, sepsis poses a serious health threat, with a high mortality rate and frequent long-term consequences for survivors. Despite its enormous burden on global healthcare and ongoing research efforts, early sepsis onset prediction remains challenging due to the complex nature of its pathophysiology. Current approaches face a fundamental trade-off: data-driven machine learning models achieve strong performance but lack interpretability, while biologically inspired models provide mechanistic insights but have limited clinical validation. In this study, we propose the

Indexed as

coupled oscillatordynamical systemselectronic health recordshybrid modelingnetwork physiologysepsis onset prediction

Identifiers

PMID42405206

DOI10.3389/fnetp.2026.1852577

PMCPMC13327882

What Socratic holds

Textmetadata

LicenceCC BY

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Registered trials

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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.