ArticleScientific reports2017
Development and Validation of a Multi-Algorithm Analytic Platform to Detect Off-Target Mechanical Ventilation.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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
15 citing papers in PubMed.
- Deep learning for time-series segmentation of mechanical ventilator waveforms.Scientific reports · 2026Article
- PVADet: fast patient-ventilator asynchrony detection on waveforms.Journal of clinical monitoring and computing · 2026Article
- Deep Learning for Time-Series Segmentation of Mechanical Ventilator Waveforms.Research square · 2025Article
- Let's get in sync: current standing and future of AI-based detection of patient-ventilator asynchrony.Intensive care medicine experimental · 2025Review
- Detection and quantitative analysis of patient-ventilator interactions in ventilated infants by deep learning networks.Pediatric research · 2024Observational
- Survey of Ventilator Waveform Interpretation Among ICU Professionals.Respiratory care · 2024Article
- Artificial intelligence in respiratory care.Frontiers in digital health · 2024Review
- Understanding basic principles of Artificial Intelligence: a practical guide for intensivists.Acta bio-medica : Atenei Parmensis · 2022Article
- Can Big Data and Machine Learning Improve Our Understanding of Acute Respiratory Distress Syndrome?Cureus · 2021Review
- Use of Machine Learning to Screen for Acute Respiratory Distress Syndrome Using Raw Ventilator Waveform Data.Critical care explorations · 2021Article
- Artificial Intelligence in the Intensive Care Unit.Critical care (London, England) · 2020Review
- Patient-ventilator asynchronies during mechanical ventilation: current knowledge and research priorities.Intensive care medicine experimental · 2019Review
- Evaluating Delivery of Low Tidal Volume Ventilation in Six ICUs Using Electronic Health Record Data.Critical care medicine · 2019Observational
- Predicting Patient-ventilator Asynchronies with Hidden Markov Models.Scientific reports · 2018Article
- Leveraging IoTs and Machine Learning for Patient Diagnosis and Ventilation Management in the Intensive Care Unit.IEEE pervasive computingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Healthcare-specific analytic software is needed to process the large volumes of streaming physiologic waveform data increasingly available from life support devices such as mechanical ventilators. Detection of clinically relevant events from these data streams will advance understanding of critical illness, enable real-time clinical decision support, and improve both clinical outcomes and patient experience. We used mechanical ventilation waveform data (VWD) as a use case to address broader issues of data access and analysis including discrimination between true events and waveform artifacts. We developed an open source data acquisition platform to acquire VWD, and a modular, multi-algorithm analytic platform (ventMAP) to enable automated detection of off-target ventilation (OTV) delivery in critically-ill patients. We tested the hypothesis that use of artifact correction logic would improve the specificity of clinical event detection without compromising sensitivity. We showed that ventMAP could accurately detect harmful forms of OTV including excessive tidal volumes and common forms of patient-ventilator asynchrony, and that artifact correction significantly improved the specificity of event detection without decreasing sensitivity. Our multi-disciplinary approach has enabled automated analysis of high-volume streaming patient waveform data for clinical and translational research, and will advance the study and management of critically ill patients requiring mechanical ventilation.
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.