Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Jun 29;23(1):441.
doi: 10.1186/s12879-023-08409-3.

Machine learning applications on neonatal sepsis treatment: a scoping review

Colleen O'Sullivan  1 , Daniel Hsiang-Te Tsai  2   3 , Ian Chang-Yen Wu  2   3   4 , Emanuela Boselli  5 , Carmel Hughes  6 , Deepak Padmanabhan  7 , Yingfen Hsia  6   2
Affiliations

Machine learning applications on neonatal sepsis treatment: a scoping review

Colleen O'Sullivan et al. BMC Infect Dis. .

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.

Keywords: Antibiotic; Antimicrobial Resistance; Bloodstream Infection; Machine Learning; Neonate.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
PRISMA flow diagram of numbers for studies identified in PubMed, Embase and Scopus
Fig. 2
Fig. 2
Schematic diagram of different applications of machine learning for neonatal sepsis including important parameters to include and which models are worth investigating for each task
See this image and copyright information in PMC

References

    1. Fleischmann-Struzek C, Goldfarb DM, Schlattmann P, Schlapbach LJ, Reinhart K, Kissoon N. The global burden of paediatric and neonatal sepsis: a systematic review. Lancet Respir Med. 2018;6(3):223–30. - PubMed
    1. Liu L, Oza S, Hogan D, Chu Y, Perin J, Zhu J, Lawn JE, Cousens S, Mathers C, Black RE. Global, regional, and national causes of under-5 mortality in 2000–2013;15: an updated systematic analysis with implications for the Sustainable Development Goals. Lancet. 2016;388(10063):3027–35. - PMC - PubMed
    1. NICE. Neonatal infection: antibiotics for prevention and treatment. NG195. 2021.
    1. Araújo BC, Guimarães H. Risk factors for neonatal sepsis: an overview. J Pediatr Neonatal Individualized Med (JPNIM) 2020;9(2):e090206.
    1. Soman M, Green B, Daling J. Risk factors for early neonatal sepsis. Am J Epidemiol. 1985;121(5):712–9. - PubMed

Publication types