Machine learning applications on neonatal sepsis treatment: a scoping review

Colleen O’Sullivan, Daniel Hsiang‐Te Tsai, Ian Chang-Yen Wu, Emanuela Boselli, Carmel M Hughes, Deepak Padmanabhan, Yingfen Hsia

BMC Infectious Diseases 2023 article cited by 29

Source Open-access PDF More in Medicine & Health

importance 7/10 ai assisted Neonatal and Maternal Infections neonatology stage: research mixed

Problem

Neonatal sepsis has non-specific signs, treatment is labour-intensive, and antimicrobial resistance complicates empiric antibiotic choice.

Why it matters

Neonatal sepsis is a major cause of mortality, with over 70% of bloodstream infections resistant to first-line antibiotics.

What the AI did

A scoping review of 18 studies examining machine learning as a tool to aid clinicians in diagnosing neonatal infections and choosing empiric antibiotic treatment.

Results

18 studies included; no performance metrics stated in abstract

Impact

Maps existing ML applications for neonatal sepsis treatment; no demonstrated clinical outcome shown.

Record

Venue	BMC Infectious Diseases
Published	2023
Work type	article
Authors	Colleen O’Sullivan, Daniel Hsiang‐Te Tsai, Ian Chang-Yen Wu, Emanuela Boselli, Carmel M Hughes, Deepak Padmanabhan, Yingfen Hsia
Citations	29 (OpenAlex, at import time)
References	59
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1186/s12879-023-08409-3
Open access	gold
Keywords	neonatal sepsis, machine learning, antibiotics, scoping review, antimicrobial resistance

Importance rating

7 / 10

significant — thousands to millions affected

Abstract states neonatal sepsis is a major cause of mortality worldwide and over 70% of bloodstream infections resist first-line antibiotics; ML could aid diagnosis and antibiotic choice.

Basis: stated. The source itself states the scale. Stated scale: over 70% of neonatal bloodstream infections resistant to first-line antibiotic treatment. Method and limits.

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