ArticleSensors (Basel, Switzerland)2019
Machine Learning Methods Applied to Predict Ventilator-Associated Pneumonia with
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 61 citations in OpenAlex.
- Artificial intelligence for infection surveillance, risk stratification, and antimicrobial decision support in acute-care hospitals: a scoping review.Frontiers in artificial intelligence · 2026Pooled it
- Exhaled Volatile Organic Compounds Detection in Pneumonia Screening: A Comprehensive Meta-analysis.Lung · 2024Pooled it
- Integrating AI into infection control: Evaluating the accuracy and consistency of four leading platforms across three regions.Journal of the Association of Medical Microbiology and Infectious Disease Canada = Journal officiel de l'Association pour la microbiologie medicale et l'infectiologie Canada · 2026Article
- Advances in Fungal Infection Research: From Novel Diagnostics to Innovative Therapeutics.Journal of fungi (Basel, Switzerland) · 2025Article
- Examination of unique volatile organic compound signatures in nasopharyngeal test swab viral transport media using an electronic nose.PloS one · 2025Article
- Ventilator-Associated Pneumonia Prediction Models Based on AI: Scoping Review.JMIR medical informatics · 2024Article
- PAM-DenseNet: A Deep Convolutional Neural Network for Computer-Aided COVID-19 Diagnosis.IEEE transactions on cybernetics · 2022Article
- Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods.Sensors (Basel, Switzerland) · 2021Review
- The smell of lung disease: a review of the current status of electronic nose technology.Respiratory research · 2021Review
- The potential of artificial intelligence to improve patient safety: a scoping review.NPJ digital medicine · 2021Article
- Digital microbiology.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2020Review
- Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography.Sensors (Basel, Switzerland) · 2020Article
- Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research.Respiratory research · 2020Article
- A Low-Cost Breath Analyzer Module in Domiciliary Non-Invasive Mechanical Ventilation for Remote COPD Patient Monitoring.Sensors (Basel, Switzerland) · 2020Article
- Article
- A Novel Framework with High Diagnostic Sensitivity for Lung Cancer Detection by Electronic Nose.Sensors (Basel, Switzerland) · 2019Article
- Special Issue "Advanced Signal Processing in Intelligent Systems for Health Monitoring".Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
Abstract
One concern to the patients is the off-line detection of pneumonia infection status after using the ventilator in the intensive care unit. Hence, machine learning methods for ventilator-associated pneumonia (VAP) rapid diagnose are proposed. A popular device, Cyranose 320 e-nose, is usually used in research on lung disease, which is a highly integrated system and sensor comprising 32 array using polymer and carbon black materials. In this study, a total of 24 subjects were involved, including 12 subjects who are infected with pneumonia, and the rest are non-infected. Three layers of back propagation artificial neural network and support vector machine (SVM) methods were applied to patients' data to predict whether they are infected with VAP with
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.