Early diagnosis of bloodstream infections in the intensive care unit using machine-learning algorithms
Abstract
Purpose We aimed to develop a machine-learning (ML) algorithm that can predict intensive care unit (ICU)-acquired bloodstream infections (BSI) among patients suspected of infection in the ICU. Methods The study was based on patients' electronic health records at Beth Israel Deaconess Medical Center (BIDMC) in Boston, Massachusetts, USA, and at Rambam Health Care Campus (RHCC), Haifa, Israel. We included adults from whom blood cultures were collected for suspected BSI at least 48 h after admission. Clinical data, including time-series variables and their interactions, were analyzed by an ML algorithm at each site. Prediction ability for ICU-acquired BSI was assessed by the area under the receiver operating characteristics (AUROC) of ten-fold cross-validation and validation sets with 95% confidence intervals. Results The datasets comprised 2351 patients from BIDMC (151 with BSI) and 1021 from RHCC (162 with BSI). The median (inter-quartile range) age was 62 (51-75) and 56 (38-69) years, respectively; the median Acute Physiology and Chronic Health Evaluation II scores were 26 (21-32) and 24 (20-29), respectively. The means of the cross-validation AUROCs were 0.87 ± 0.02 for BIDMC and 0.93 ± 0.03 for RHCC. AUROCs of 0.89 ± 0.01 and 0.92 ± 0.02 were maintained in both centers with internal validation, while external validation deteriorated. Valuable predictors were mainly the trends of time-series variables such as laboratory results and vital signs. Conclusion An ML approach that…
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Most 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 [14]
Machine 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 [14]
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 [14]
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References 37 · Cited by 91
- Authors
- Roimi, M., Neuberger, A., Shrot, A., Paul, M., Geffen, Y., & Bar-Lavie, Y.
- Affiliations
- Rambam Health Care Campus (IL)
- Corresponding author
- Roimi, Michael
- Venue
- Intensive Care Medicine
- Published
- 2020-01-07
- Volume
- 46
- Issue
- 3
- Pages
- 454-462
- ISSN
- 0342-4642 (ISSN-L)
- 1432-1238
- OpenAlex
- W3000574586
- Cited by
- 91
- References
- 37
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 20 · 2023: 20 · 2022: 18 · 2021: 9 · 2020: 6
- FWCI
- 5.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Intensive Care Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- bloodstream infection, intensive care unit infections, early diagnosis, machine learning prediction, intensive care unit, blood culture, laboratory findings, vital signs, external validation, APACHE II score
- MeSH terms
- Machine Learning, Adult, Algorithms, Boston, Humans, Intensive Care Units, Massachusetts, Bacteremia, Sepsis, Early Diagnosis
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