A machine learning model for the early diagnosis of bloodstream infection in patients admitted to the pediatric intensive care unit
Open accessAbstract
Bloodstream infection (BSI) is associated with increased morbidity and mortality in the pediatric intensive care unit (PICU) and high healthcare costs. Early detection and appropriate treatment of BSI may improve patient’s outcome. Data on machine-learning models to predict BSI in pediatric patients are limited and neither study included time series data. We aimed to develop a machine learning model to predict an early diagnosis of BSI in patients admitted to the PICU. This was a retrospective cohort study of patients who had at least one positive blood culture result during stay at a PICU of a tertiary-care university hospital, from January 1 st to December 31 st 2019. Patients with positive blood culture results with growth of contaminants and those with incomplete data were excluded. Models were developed using demographic, clinical and laboratory data collected from the electronic medical record. Laboratory data (complete blood cell counts with differential and C-reactive protein) and vital signs (heart rate, respiratory rate, blood pressure, temperature, oxygen saturation) were obtained 72 hours before and on the day of blood culture collection. A total of 8816 data from 76 patients were processed by the models. The machine committee was the best-performing model, showing accuracy of 99.33%, precision of 98.89%, sensitivity of 100% and specificity of 98.46%. Hence, we developed a model using demographic, clinical and laboratory data collected on a routine basis that was able to detect BSI with excellent accuracy and precision, and high sensitivity and specificity. The inclusion of vital signs and laboratory data variation over time allowed the model to identify temporal changes that could be suggestive of the diagnosis of BSI. Our model might help the medical team in clinical-decision making by creating an alert in the electronic medical record, which may allow early antimicrobial initiation and better outcomes.
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Recent studies have explored various ML algorithms, such as logistic regression, random forest, support vector machines, and gradient boosting, to assess the risk of BSIs in real time.
“Recent studies have explored various ML algorithms, such as logistic regression, random forest, support vector machines, and gradient boosting, to assess the risk of BSIs in real time [10–14].”
From Early prediction of bloodstream infections in ICU patients using machine learning methods based on routine laboratory parameters (2025) · cites this paper as [10]
The Methods (Data processing; Model training and testing, page not provided) describe logistic regression and gradient boosting models for classifying patients with or without BSI. The Discussion (page not provided) reports a random-forest model for predicting central line-associated BSI. Our copy does not identify a support vector machine model, and it describes automatic evaluation as something the trained model could do, rather than real-time risk assessment that these studies performed. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMost reported ML-based BSI prediction models integrate a combination of vital signs, clinical scores, and laboratory values.
“However, most reported ML-based BSI prediction models integrate a combination of vital signs, clinical scores, and laboratory values.”
From Early prediction of bloodstream infections in ICU patients using machine learning methods based on routine laboratory parameters (2025) · cites this paper as [10]
The Methods (Model development; page not available) say this paper’s model combines clinical data, vital signs, and laboratory data. The Discussion describes several other BSI prediction models with differing inputs, but does not establish that most reported models combine all three categories or use clinical scores. (LLM, conf=0.84)
Experimental — accuracy not yet measuredMachine learning models have demonstrated significant potential in predicting BSIs by analyzing readily available clinical and laboratory data.
“Machine learning models have demonstrated significant potential in predicting BSIs by analyzing readily available clinical and laboratory data.”
From Early prediction of bloodstream infections in ICU patients using machine learning methods based on routine laboratory parameters (2025) · cites this paper as [10]
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Logistic regression in the medical literature:
Bagley, S. C., White, H., & Golomb, B. A. (2001) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(01)00372-9 · Cited by 616 · see citing works
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Sorted by citations
Chen, Y., Chen, Z., Xu, D., Li, S., Chen, F., Yu, X., Cai, S., Zeng, C., Ye, X., Yang, J., et al. (2025) · BMC Infectious Diseases · doi:10.1186/s12879-025-11580-4 · Cited by 4 · see citing works
- Authors
- Liporaci, F., Carlotti, D., & Carlotti, A.
- Affiliations
- Universidade de São Paulo (BR)
- Corresponding author
- Carlotti, Ana
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2024-05-01
- Volume
- 19
- Issue
- 5
- Pages
- e0299884
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC11062549
- OpenAlex
- W4396566652
- Cited by
- 4
- References
- 1
- Citations by year
- 2026: 1 · 2025: 3
- FWCI
- 1.21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (001)
- Topics
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- bloodstream infection, early diagnosis, pediatric intensive care unit, machine learning, ensemble classifiers, blood culture, vital signs, laboratory findings, C-reactive protein, electronic health record alerts, clinical decision support, pediatric patients
- MeSH terms
- Machine Learning, Adolescent, Child, Child, Preschool, Female, Humans, Infant, Infant, Newborn, Male, Retrospective Studies, Intensive Care Units, Pediatric, Bacteremia, Sepsis, Early Diagnosis
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