ArticleBMC pulmonary medicine2022
Early prediction of ventilator-associated pneumonia in critical care patients: a machine learning model.
Article in BMC pulmonary medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 47 citations in OpenAlex.
- Bayesian network model for identification of factors associated with ventilator-associated pneumonia in mechanically ventilated patients in the ICU: retrospective cohort study.BMC pulmonary medicine · 2026Article
- [A Transformer-based multimodal model for predicting hospital-acquired infections using imaging and clinical laboratory data].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- Developing and externally validating machine learning models to forecast short-term risk of ventilator-associated pneumonia.medRxiv : the preprint server for health sciences · 2026Article
- Real-time prediction of ventilator-associated pneumonia onset in ICU: development of a dynamic machine learning model.Journal of anesthesia, analgesia and critical care · 2026Article
- The impact of traumatic injury on the respiratory system; a narrative review of injury-associated and clinically-induced mechanisms of trauma-associated pneumonia.Frontiers in medicine · 2026Review
- Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical Outcomes.JAMA network open · 2025Article
- The future of healthcare-associated infection surveillance: Automated surveillance and using the potential of artificial intelligence.Journal of internal medicine · 2025Review
- Development and validation of a predictive model for ventilator-associated pneumonia in patients with cardiogenic shock: based on the MIMIC-IV database.Journal of thoracic disease · 2025Article
- An Innovative Deep Learning Approach for Ventilator-Associated Pneumonia (VAP) Prediction in Intensive Care Units-Pneumonia Risk Evaluation and Diagnostic Intelligence via Computational Technology (PREDICT).Journal of clinical medicine · 2025Article
- Comparing large scale and selected feature learning for community acquired pneumonia prognosis prediction using clinical data: a stacked ensemble approach.Scientific reports · 2025Article
- Predicting nosocomial pneumonia of patients with acute brain injury in intensive care unit using machine-learning models.Frontiers in medicine · 2025Article
- Enhancing machine learning performance in cardiac surgery ICU: Hyperparameter optimization with metaheuristic algorithm.PloS one · 2025Article
- Advances in Machine Learning for Mechanically Ventilated Patients.International journal of general medicine · 2025Review
- Ventilator-associated pneumonia: pathobiological heterogeneity and diagnostic challenges.Nature communications · 2024Review
- Ventilator-Associated Pneumonia Prediction Models Based on AI: Scoping Review.JMIR medical informatics · 2024Article
- Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Beyond Random Splitting: Evaluating the Impact of Data Partitioning Strategies on Ventilator-Associated Pneumonia Prediction Using EHRs.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- A machine learning-based prediction of hospital mortality in mechanically ventilated ICU patients.PloS one · 2024Article
- Machine learning to identify risk factors associated with the development of ventilated hospital-acquired pneumonia and mortality: implications for antibiotic therapy selection.Frontiers in medicine · 2023Article
- Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach.Cardiovascular diabetology · 2022Observational
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundThis study was performed to develop and validate machine learning models for early detection of ventilator-associated pneumonia (VAP) 24 h before diagnosis, so that VAP patients can receive early intervention and reduce the occurrence of complications. PATIENTS AND
methodsThis study was based on the MIMIC-III dataset, which was a retrospective cohort. The random forest algorithm was applied to construct a base classifier, and the area under the receiver operating characteristic curve (AUC), sensitivity and specificity of the prediction model were evaluated. Furthermore, We also compare the performance of Clinical Pulmonary Infection Score (CPIS)-based model (threshold value ≥ 3) using the same training and test data sets.
resultsIn total, 38,515 ventilation sessions occurred in 61,532 ICU admissions. VAP occurred in 212 of these sessions. We incorporated 42 VAP risk factors at admission and routinely measured the vital characteristics and laboratory results. Five-fold cross-validation was performed to evaluate the model performance, and the model achieved an AUC of 84% in the validation, 74% sensitivity and 71% specificity 24 h after intubation. The AUC of our VAP machine learning model is nearly 25% higher than the CPIS model, and the sensitivity and specificity were also improved by almost 14% and 15%, respectively.
conclusionsWe developed and internally validated an automated model for VAP prediction using the MIMIC-III cohort. The VAP prediction model achieved high performance based on its AUC, sensitivity and specificity, and its performance was superior to that of the CPIS model. External validation and prospective interventional or outcome studies using this prediction model are envisioned as future work.
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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.