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References from Predictive performance of machine learning models for bloodstream infection by validation type: A systematic review and meta-analysis. Local targets link to admitted publications; unresolved targets remain external evidence.
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Clinical prediction rule is more useful than qSOFA and the Sepsis-3 definition of sepsis for screening bacteremia
10.1016/j.ajem.2021.03.023 · 2021 · External reference
The ability of Procalcitonin, lactate, white blood cell count and neutrophil-lymphocyte count ratio to predict blood stream infection. Analysis of a large database
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Advances in diagnosis and prognosis of bacteraemia, bloodstream infection, and sepsis using machine learning: a comprehensive living literature review
2025 · External reference
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2019 · External reference
A Review of feature selection methods for machine learning-based disease risk prediction
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Unresolved reference
2019 · External reference
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10.1136/bmj.b375 · 2009 · External reference
An artificial intelligence approach to bloodstream infections prediction
10.3390/jcm10132901 · 2021 · External reference
Using Machine Learning Algorithms to Predict Candidaemia in ICU patients with New-Onset Systemic Inflammatory Response Syndrome
2021 · External reference
Prediction of bacteremia at the emergency department during triage and disposition stages using machine learning models
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Development of an artificial intelligence bacteremia prediction model and evaluation of its impact on physician predictions focusing on uncertainty
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10.1016/j.jclinepi.2019.12.023 · 2020 · External reference
A risk prediction model for screening bacteremic patients: A cross sectional study
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10.1038/s41598-018-30236-9 · 2018 · External reference
Early detection of bacteraemia using ten clinical variables with an artificial neural network approach
10.3390/jcm8101592 · 2019 · External reference
Accurate prediction of blood culture outcome in the intensive care unit using long short-term memory neural networks
10.1016/j.artmed.2018.10.008 · 2019 · External reference
The development and validation of a machine learning model to predict bacteremia and fungemia in hospitalized patients using electronic health record data
10.1097/ccm.0000000000004556 · 2020 · External reference
Prediction of blood culture outcome using hybrid neural network model based on electronic health records
10.1186/s12911-020-1113-4 · 2020 · External reference
Early diagnosis of bloodstream infections in the intensive care unit using machine-learning algorithms
10.1007/s00134-019-05876-8 · 2020 · External reference
Diagnosing hospital bacteraemia in the framework of predictive, preventive and personalised medicine using electronic health records and machine learning classifiers
10.1007/s13167-021-00252-3 · 2021 · External reference
Developing Machine-Learning Prediction Algorithm for Bacteremia in Admitted patients
10.2147/idr.s293496 · 2021 · External reference
Four biomarkers-based artificial neural network model for accurate early prediction of bacteremia with low-level procalcitonin
2021 · External reference
Using machine learning to predict blood culture outcomes in the emergency department: a single-centre, retrospective, observational study
10.1136/bmjopen-2021-053332 · 2022 · External reference
10.3390/diagnostics12102498
10.3390/diagnostics12102498 · External reference
Prediction of bacteremia based on 12-year medical data using a machine learning approach: Effect of medical data by extraction time
2022 · External reference
Bacteremia detection from complete blood count and differential leukocyte count with machine learning: complementary and competitive with C-reactive protein and procalcitonin tests
10.1186/s12879-022-07223-7 · 2022 · External reference
Diagnostic stewardship for blood cultures in the emergency department: a multicenter validation and prospective evaluation of a machine learning prediction tool
10.1016/j.ebiom.2022.104176 · 2022 · External reference
Machine learning of cell population data, complete blood count, and differential count parameters for early prediction of bacteremia among adult patients with suspected bacterial infections and blood culture sampling in emergency departments
10.1016/j.jmii.2023.05.001 · 2023 · External reference
Real-time artificial intelligence system for bacteremia prediction in adult febrile emergency department patients
10.1016/j.ijmedinf.2023.105176 · 2023 · External reference
Combination of machine learning algorithms with natural language processing may increase the probability of bacteremia detection in the emergency department: A retrospective, big-data analysis of 94,482 patients
2024 · External reference
Using machine learning to predict bacteremia in urgent care patients on the basis of triage data and laboratory results
10.1016/j.ajem.2024.08.045 · 2024 · External reference
Prediction of carbapenem-resistant gram-negative bacterial bloodstream infection in intensive care unit based on machine learning
10.1186/s12911-024-02504-4 · 2024 · External reference
A machine learning predictive model of bloodstream infection in hospitalized patients
2024 · External reference
A privacy-preserving platform oriented medical healthcare and its application in identifying patients with candidemia
10.1038/s41598-024-66596-8 · 2024 · External reference
Quantification of identifying cognitive impairment using olfactory-stimulated functional near-infrared spectroscopy with machine learning: a post hoc analysis of a diagnostic trial and validation of an external additional trial
10.1186/s13195-023-01268-9 · 2023 · External reference
Internal and external validation of machine learning models for predicting acute kidney injury following non-cardiac surgery using open datasets
10.3390/jpm14060587 · 2024 · External reference
Key challenges for delivering clinical impact with artificial intelligence
10.1186/s12916-019-1426-2 · 2019 · External reference
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models
10.1016/j.jclinepi.2019.02.004 · 2019 · External reference
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10.1038/s41591-022-01772-9 · 2022 · External reference
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10.2196/medinform.5909 · 2016 · External reference
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Challenge-enabled machine learning to drug-response prediction
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Machine learning strategies to tackle data challenges in mass spectrometry-based proteomics
10.1021/jasms.4c00180 · 2024 · External reference
Decision curve analysis: a novel method for evaluating prediction models
10.1177/0272989x06295361 · 2006 · External reference
Calibration of risk prediction models: impact on decision-analytic performance
10.1177/0272989x14547233 · 2015 · External reference
