Evidence map›Paper›PMID 39892951›Full record

ReviewClinics in perinatology2025

Machine Learning Models as Early Warning Systems for Neonatal Infection.

Brynne A Sullivan, Robert W Grundmeier

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Clinics in perinatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

2 authors.

Brynne A SullivanDivision of Neonatology, Department of Pediatrics, University of Virginia School of Medicine, 1215 Lee Street, P.O. Box 800386, Charlottesville, VA 22947, USA. Electronic address: Brynne@virginia.edu.
Robert W GrundmeierDepartment of Pediatrics, Perelman School of Medicine at the University of Pennsylvania; Division of Clinical Informatics, Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, 3400 Civic Center Boulevard Ste 10, Philadelphia, PA 19104, USA.

Funding

Predictive Informatics Monitoring in the Neonatal Intensive Care UnitR01HD072071 · NICHD · UNIVERSITY OF VIRGINIA · PI KAREN D FAIRCHILD, Brynne Archer Sullivan · 2014 to 2026
$7.6M
NICHD NIH HHS R01 HD072071
6 · The paper itself

Abstract

Neonatal infections pose a significant threat to the health of newborns. Associated morbidity and mortality risks underscore the urgency of prompt diagnosis and treatment with appropriate empiric antibiotics. Delay in treatment can be fatal; thus, early detection improves outcomes. However, diagnosing early is a challenge as signs and symptoms of neonatal infection are non-specific and overlap with non-infectious conditions. Machine learning (ML) offers promise in early detection, utilizing various data sources and methodologies. However, ML models require rigorous validation and consideration of various challenges, including false alarms and user acceptance requiring careful integration and ongoing evaluation for successful implementation.

Indexed as

Machine LearningNeonatal SepsisEarly DiagnosisHumansInfant, NewbornMachine learningNeonatal sepsisPredicting outcomesPrematurityWarning systems

Identifiers

PMID39892951
PMCPMC13150861

What OpenQuestion holds

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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 OpenQuestion graph.