ArticleIntensive care medicine2020
Early diagnosis of bloodstream infections in the intensive care unit using machine-learning algorithms.
Article in Intensive care medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.
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Who cites it
41 citing papers in PubMed, 2 syntheses or guidelines pooled it, 97 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
- Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis.BMC infectious diseases · 2023Pooled it
- Development and validation of a machine learning model for predicting positive blood cultures using vital signs in ICU patients.Scientific reports · 2026Article
- Early prediction of bloodstream infections in ICU patients using machine learning methods based on routine laboratory parameters.BMC infectious diseases · 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
- Development and application of an early prediction model for risk of bloodstream infection based on real-world study.BMC medical informatics and decision making · 2025Article
- Predicting appropriateness of antibiotic treatment among ICU patients with hospital-acquired infection.NPJ digital medicine · 2025Article
- The association between defecation frequency and mortality in critically ill patients with suspected sepsis in Israel.Acute and critical care · 2025Article
- Early diagnosis and prognostic prediction of secondary bloodstream infections caused byFrontiers in cellular and infection microbiology · 2025Article
- Advancing infection prevention and control through artificial intelligence: a scoping review of applications, barriers, and a decision-support checklist.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2025Article
- Leveraging explainable artificial intelligence for early prediction of bloodstream infections using historical electronic health records.PLOS digital health · 2024Article
- Development and validation of machine learning models to predict MDRO colonization or infection on ICU admission by using electronic health record data.Antimicrobial resistance and infection control · 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 model for the early diagnosis of bloodstream infection in patients admitted to the pediatric intensive care unit.PloS one · 2024Article
- Development and validation a nomogram prediction model for early diagnosis of bloodstream infections in the intensive care unit.Frontiers in cellular and infection microbiology · 2024Article
- In-hospital real-time prediction of COVID-19 severity regardless of disease phase using electronic health records.PloS one · 2024Article
- Assessing sepsis-induced immunosuppression to predict positive blood cultures.Frontiers in immunology · 2024Article
- Carbapenem-resistant Gram-negative bacteria (CR-GNB) in ICUs: resistance genes, therapeutics, and prevention - a comprehensive review.Frontiers in public health · 2024Review
- The application of artificial intelligence in the management of sepsis.Medical review (2021) · 2023Review
- Development of an artificial intelligence bacteremia prediction model and evaluation of its impact on physician predictions focusing on uncertainty.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWe aimed to develop a machine-learning (ML) algorithm that can predict intensive care unit (ICU)-acquired bloodstream infections (BSI) among patients suspected of infection in the ICU.
methodsThe study was based on patients' electronic health records at Beth Israel Deaconess Medical Center (BIDMC) in Boston, Massachusetts, USA, and at Rambam Health Care Campus (RHCC), Haifa, Israel. We included adults from whom blood cultures were collected for suspected BSI at least 48 h after admission. Clinical data, including time-series variables and their interactions, were analyzed by an ML algorithm at each site. Prediction ability for ICU-acquired BSI was assessed by the area under the receiver operating characteristics (AUROC) of ten-fold cross-validation and validation sets with 95% confidence intervals.
resultsThe datasets comprised 2351 patients from BIDMC (151 with BSI) and 1021 from RHCC (162 with BSI). The median (inter-quartile range) age was 62 (51-75) and 56 (38-69) years, respectively; the median Acute Physiology and Chronic Health Evaluation II scores were 26 (21-32) and 24 (20-29), respectively. The means of the cross-validation AUROCs were 0.87 ± 0.02 for BIDMC and 0.93 ± 0.03 for RHCC. AUROCs of 0.89 ± 0.01 and 0.92 ± 0.02 were maintained in both centers with internal validation, while external validation deteriorated. Valuable predictors were mainly the trends of time-series variables such as laboratory results and vital signs.
conclusionAn ML approach that uses temporal and site-specific data achieved high performance in recognizing BC samples with a high probability for ICU-acquired BSI.
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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.