ArticleCritical care medicine2020
The Development and Validation of a Machine Learning Model to Predict Bacteremia and Fungemia in Hospitalized Patients Using Electronic Health Record Data.
Article in Critical care medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Clinical predictive models of invasive Candida infection: A systematic literature review.Medical mycology · 2021Pooled it
- Machine learning model based on clinical and imaging features for predicting fungal infections in children with leukemia.BMC pediatrics · 2026Article
- Development and validation of a bacteremia prediction score for adult emergency department patients using hybrid machine learning and regression.Scientific reports · 2026Article
- Intuitive Risk Equation for Post-Transplant Bloodstream Infection Prediction: A Symbolic Regression Approach.Biomedicines · 2026Article
- Machine learning triumphs in differentiating fungal and bacterial infections.BMC infectious diseases · 2026Article
- Guiding Antibiotic Therapy with Machine Learning: Real-World Applications of a CDSS in Bacteremia Management.Life (Basel, Switzerland) · 2025Article
- Multivariate protein landscape of host response in hospitalised patients with suspected infection in the emergency department.Nature communications · 2025Article
- 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
- Leveraging innovative diagnostics as a tool to contain superbugs.Antonie van Leeuwenhoek · 2025Review
- Epidemiological shifts and advances in research on early diagnosis of invasive fungal infection in critically ill patients.Frontiers in cellular and infection microbiology · 2025Review
- 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
- Distinct immune profiles and clinical outcomes in sepsis subphenotypes based on temperature trajectories.Intensive care medicine · 2024Article
- Leveraging explainable artificial intelligence for early prediction of bloodstream infections using historical electronic health records.PLOS digital health · 2024Article
- Early detection of bloodstream infection in critically ill children using artificial intelligence.Acute and critical care · 2024Article
- When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 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
- A machine learning model for early candidemia prediction in the intensive care unit: Clinical application.PloS one · 2024Article
- Characterization of the Pathogen Distribution and Drug Resistance in Bloodstream Infections During COVID-19 Pandemic in a Tertiary Hospital in Eastern China: Comparison with the Pre-Pandemic Period.Infection and drug resistance · 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
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesBacteremia and fungemia can cause life-threatening illness with high mortality rates, which increase with delays in antimicrobial therapy. The objective of this study is to develop machine learning models to predict blood culture results at the time of the blood culture order using routine data in the electronic health record.
designRetrospective analysis of a large, multicenter inpatient data.
settingTwo academic tertiary medical centers between the years 2007 and 2018. SUBJECTS: All hospitalized patients who received a blood culture during hospitalization.
interventionsThe dataset was partitioned temporally into development and validation cohorts: the logistic regression and gradient boosting machine models were trained on the earliest 80% of hospital admissions and validated on the most recent 20%. MEASUREMENTS AND MAIN
resultsThere were 252,569 blood culture days-defined as nonoverlapping 24-hour periods in which one or more blood cultures were ordered. In the validation cohort, there were 50,514 blood culture days, with 3,762 cases of bacteremia (7.5%) and 370 cases of fungemia (0.7%). The gradient boosting machine model for bacteremia had significantly higher area under the receiver operating characteristic curve (0.78 [95% CI 0.77-0.78]) than the logistic regression model (0.73 [0.72-0.74]) (p < 0.001). The model identified a high-risk group with over 30 times the occurrence rate of bacteremia in the low-risk group (27.4% vs 0.9%; p < 0.001). Using the low-risk cut-off, the model identifies bacteremia with 98.7% sensitivity. The gradient boosting machine model for fungemia had high discrimination (area under the receiver operating characteristic curve 0.88 [95% CI 0.86-0.90]). The high-risk fungemia group had 252 fungemic cultures compared with one fungemic culture in the low-risk group (5.0% vs 0.02%; p < 0.001). Further, the high-risk group had a mortality rate 60 times higher than the low-risk group (28.2% vs 0.4%; p < 0.001).
conclusionsOur novel models identified patients at low and high-risk for bacteremia and fungemia using routinely collected electronic health record data. Further research is needed to evaluate the cost-effectiveness and impact of model implementation in clinical practice.
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