ArticleScientific reports2018
Machine learning for fast identification of bacteraemia in SIRS patients treated on standard care wards: a cohort study.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysis.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Pooled it
- A rapidly personalized in-hospital bloodstream infection prediction model: a multicenter retrospective study.BMC infectious diseases · 2026Article
- Intuitive Risk Equation for Post-Transplant Bloodstream Infection Prediction: A Symbolic Regression Approach.Biomedicines · 2026Article
- Leveraging innovative diagnostics as a tool to contain superbugs.Antonie van Leeuwenhoek · 2025Review
- Gut Microbiota Dysbiosis Facilitates Susceptibility to Bloodstream Infection.Journal of microbiology (Seoul, Korea) · 2024Article
- Leveraging explainable artificial intelligence for early prediction of bloodstream infections using historical electronic health records.PLOS digital health · 2024Article
- A Machine Learning Predictive Model of Bloodstream Infection in Hospitalized Patients.Diagnostics (Basel, Switzerland) · 2024Article
- A Simple Bacteremia Score for Predicting Bacteremia in Patients with Suspected Infection in the Emergency Department: A Cohort Study.Journal of personalized medicine · 2023Article
- A machine learning approach using endpoint adjudication committee labels for the identification of sepsis predictors at the emergency department.BMC emergency medicine · 2022Article
- Development of a risk prediction model for bloodstream infection in patients with fever of unknown origin.Journal of translational medicine · 2022Article
- Routine laboratory biomarkers used to predict Gram-positive or Gram-negative bacteria involved in bloodstream infections.Scientific reports · 2022Article
- Accurate long-term prediction of death for patients with cirrhosis.Hepatology (Baltimore, Md.) · 2022Article
- Bacteremia detection from complete blood count and differential leukocyte count with machine learning: complementary and competitive with C-reactive protein and procalcitonin tests.BMC infectious diseases · 2022Article
- Observational
- Prediction of Bacteremia Based on 12-Year Medical Data Using a Machine Learning Approach: Effect of Medical Data by Extraction Time.Diagnostics (Basel, Switzerland) · 2022Article
- Machine learning identification of specific changes in myeloid cell phenotype during bloodstream infections.Scientific reports · 2021Article
- An Artificial Intelligence Approach to Bloodstream Infections Prediction.Journal of clinical medicine · 2021Article
- Predicting survival after liver transplantation in patients with hepatocellular carcinoma using the LiTES-HCC score.Journal of hepatology · 2021Article
- Developing Machine-Learning Prediction Algorithm for Bacteremia in Admitted Patients.Infection and drug resistance · 2021Article
- 'No growth to date'? Predicting positive blood cultures in critical illness.Intensive care medicine · 2020Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bacteraemia is a life-threating condition requiring immediate diagnostic and therapeutic actions. Blood culture (BC) analyses often result in a low true positive result rate, indicating its improper usage. A predictive model might assist clinicians in deciding for whom to conduct or to avoid BC analysis in patients having a relevant bacteraemia risk. Predictive models were established by using linear and non-linear machine learning methods. To obtain proper data, a unique data set was collected prior to model estimation in a prospective cohort study, screening 3,370 standard care patients with suspected bacteraemia. Data from 466 patients fulfilling two or more systemic inflammatory response syndrome criteria (bacteraemia rate: 28.8%) were finally used. A 29 parameter panel of clinical data, cytokine expression levels and standard laboratory markers was used for model training. Model tuning was performed in a ten-fold cross validation and tuned models were validated in a test set (80:20 random split). The random forest strategy presented the best result in the test set validation (ROC-AUC: 0.729, 95%CI: 0.679-0.779). However, procalcitonin (PCT), as the best individual variable, yielded a similar ROC-AUC (0.729, 95%CI: 0.679-0.779). Thus, machine learning methods failed to improve the moderate diagnostic accuracy of PCT.
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