ArticleInfection and drug resistance2021
Developing Machine-Learning Prediction Algorithm for Bacteremia in Admitted Patients.
Article in Infection and drug resistance, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 40 citations in OpenAlex.
- Intuitive Risk Equation for Post-Transplant Bloodstream Infection Prediction: A Symbolic Regression Approach.Biomedicines · 2026Article
- Developing Machine-Learning Models to Predict Bacteremia in Febrile Adults Presenting to the Emergency Department: A Retrospective Cohort Study from a Large Center.The western journal of emergency medicine · 2025Article
- Data to diagnosis: Machine learning-aided prediction of bloodstream infections.Bioinformation · 2025Article
- Bacteremia in the Gulf Cooperation Council Region: A Review of the Literature 2013-2023.Infection and drug resistance · 2025Review
- Leveraging explainable artificial intelligence for early prediction of bloodstream infections using historical electronic health records.PLOS digital health · 2024Article
- Prediction of carbapenem-resistant gram-negative bacterial bloodstream infection in intensive care unit based on machine learning.BMC medical informatics and decision making · 2024Article
- A Machine Learning Predictive Model of Bloodstream Infection in Hospitalized Patients.Diagnostics (Basel, Switzerland) · 2024Article
- Clinical predictors of hospital-acquired bloodstream infections: A healthcare system analysis.Spartan medical research journal · 2024Article
- Machine learning model for the prediction of gram-positive and gram-negative bacterial bloodstream infection based on routine laboratory parameters.BMC infectious diseases · 2023Article
- Routine laboratory biomarkers used to predict Gram-positive or Gram-negative bacteria involved in bloodstream infections.Scientific reports · 2022Article
- [New predictive models of bacteremia in the emergency department: a step forward].Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2022Review
- Bandemia as an Early Predictive Marker of Bacteremia: A Retrospective Cohort Study.International journal of environmental research and public health · 2022Observational
- Predicting bloodstream infection outcome using machine learning.Scientific reports · 2021Article
- An Artificial Intelligence Approach to Bloodstream Infections Prediction.Journal of clinical medicine · 2021Article
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Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeBloodstream infection among hospitalized patients is associated with serious adverse outcomes. Blood culture is routinely ordered in patients with suspected infections, although 90% of blood cultures do not show any growth of organisms. The evidence regarding the prediction of bacteremia is scarce. PATIENTS AND
methodsA retrospective review of blood cultures requested for a cohort of admitted patients between 2017 and 2019 was undertaken. Several machine-learning models were used to identify the best prediction model. Additionally, univariate and multivariable logistic regression was used to determine the predictive factors for bacteremia.
resultsA total of 36,405 blood cultures of 7157 patients were done. There were 2413 (6.62%) positive blood cultures. The best prediction was by using NN with the high specificity of 88% but low sensitivity. There was a statistical difference in the following factors: longer admission days before the blood culture, presence of a central line, and higher lactic acid-more than 2 mmol/L.
conclusionDespite the low positive rate of blood culture, machine learning could predict positive blood culture with high specificity but minimum sensitivity. Yet, the SIRS score, qSOFA score, and other known factors were not good prognostic factors. Further improvement and training would possibly enhance machine-learning performance.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.