Machine learning applications on neonatal sepsis treatment: a scoping review

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Introduction
Neonatal sepsis is a major cause of health loss and mortality worldwide. Without proper treatment, neonatal sepsis can quickly develop into multisystem organ failure. However, the signs of neonatal sepsis are non-specific, and treatment is labour-intensive and expensive. Moreover, antimicrobial resistance is a significant threat globally, and it has been reported that over 70% of neonatal bloodstream infections are resistant to first-line antibiotic treatment. Machine learning is a potential tool to aid clinicians in diagnosing infections and in determining the most appropriate empiric antibiotic treatment, as has been demonstrated for adult populations. This review aimed to present the application of machine learning on neonatal sepsis treatment.

Methods
PubMed, Embase, and Scopus were searched for studies published in English focusing on neonatal sepsis, antibiotics, and machine learning.

Results
There were 18 studies included in this scoping review. Three studies focused on using machine learning in antibiotic treatment for bloodstream infections, one focused on predicting in-hospital mortality associated with neonatal sepsis, and the remaining studies focused on developing machine learning prediction models to diagnose possible sepsis cases. Gestational age, C-reactive protein levels, and white blood cell count were important predictors to diagnose neonatal sepsis. Age, weight, and days from hospital admission to blood sample taken were important to predict antibiotic-resistant infections. The best-performing machine learning models were random forest and neural networks.

Conclusion
Despite the threat antimicrobial resistance poses, there was a lack of studies focusing on the use of machine learning for aiding empirical antibiotic treatment for neonatal sepsis.

Original languageEnglish
Article number441
Number of pages10
JournalBMC Infectious Diseases
Volume23
DOIs
Publication statusPublished - 29 Jun 2023

Keywords

  • Machine Learning
  • Antimicrobial Resistance
  • Bloodstream Infection
  • Antibiotic
  • Neonate

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