ArticleDiagnostics (Basel, Switzerland)2024
A Machine Learning Predictive Model of Bloodstream Infection in Hospitalized Patients.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Diagnostic Performance of Machine Learning Models for Predicting Bloodstream Infections in Large Cohorts: A Systematic Review and Meta-analysis.Infectious diseases and therapy · 2026Pooled it
- Building the adult growth hormone deficiency data mart: a Real-World model of AI-driven clinical data extraction in a single Italian center.Journal of endocrinological investigation · 2026Article
- Evaluation of machine learning pipeline for blood culture outcome prediction on prospectively collected emergency department data.Journal of medical microbiology · 2026Article
- An interpretable machine-learning model for post-admission reassessment of bloodstream infection risk in ICU patients with pneumonia.Scientific reports · 2026Article
- Artificial Intelligence in Dialysis Therapies:Saudi medical journal · 2026Review
- Healthcare-associated infections in Italian long-term care facilities: a machine learning analysis of a 12-month cohort.Infection control and hospital epidemiology · 2026Article
- Performance of predictive AI-based clinical decision support systems across clinical domains: A systematic review and meta-analysis.PLOS digital health · 2026Article
- From Narrative to Navigation-A Translational Roadmap for AI-enabled Early Sepsis Prediction: Comment on Shanmugam et al.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2025Article
- The Paradox of Healthcare in the 'Superbugs' Era: Current Challenges and Future Directions.Pathogens (Basel, Switzerland) · 2025Review
- Cytokine-Based Insights into Bloodstream Infections and Bacterial Gram Typing in ICU COVID-19 Patients.Metabolites · 2025Article
- The role of rapid multiplex molecular syndromic panels in the clinical management of infections in critically ill patients: an experts-opinion document.Critical care (London, England) · 2024Review
- Leveraging explainable artificial intelligence for early prediction of bloodstream infections using historical electronic health records.PLOS digital health · 2024Article
- Secondary Bacterial Infections in Critically Ill COVID-19 Patients Admitted in the Intensive Care Unit of a Tertiary Hospital in Romania.Journal of clinical medicine · 2024Article
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Corrections and comments
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of the study was to build a machine learning-based predictive model to discriminate between hospitalized patients at low risk and high risk of bloodstream infection (BSI). A Data Mart including all patients hospitalized between January 2016 and December 2019 with suspected BSI was built. Multivariate logistic regression was applied to develop a clinically interpretable machine learning predictive model. The model was trained on 2016-2018 data and tested on 2019 data. A feature selection based on a univariate logistic regression first selected candidate predictors of BSI. A multivariate logistic regression with stepwise feature selection in five-fold cross-validation was applied to express the risk of BSI. A total of 5660 hospitalizations (4026 and 1634 in the training and the validation subsets, respectively) were included. Eleven predictors of BSI were identified. The performance of the model in terms of AUROC was 0.74. Based on the interquartile predicted risk score, 508 (31.1%) patients were defined as being at low risk, 776 (47.5%) at medium risk, and 350 (21.4%) at high risk of BSI. Of them, 14.2% (72/508), 30.8% (239/776), and 64% (224/350) had a BSI, respectively. The performance of the predictive model of BSI is promising. Computational infrastructure and machine learning models can help clinicians identify people at low risk for BSI, ultimately supporting an antibiotic stewardship approach.
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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.