SynthesisRespiratory research2024
A systematic review of machine learning models for management, prediction and classification of ARDS.
Synthesis in Respiratory research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
What it found
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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
18 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Early Prediction of Mortality Risk in Acute Respiratory Distress Syndrome: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Accuracy of artificial intelligence algorithms in predicting acute respiratory distress syndrome: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- [Construction and validation of a predictive model for the risk of ARDS in severely burned patients].Zhonghua shao shang yu chuang mian xiu fu za zhi · 2026Article
- Article
- Contrastive Transformer-Driven Discovery of Temporal Hemodynamic Subphenotypes in Cardiac Surgery Patients.medRxiv : the preprint server for health sciences · 2026Article
- The regulation of artificial intelligence in intensive care units: from narrow tools to generalist systems.NPJ digital medicine · 2026Review
- Machine learning in ARDS: an intensivist's guide to artificial intelligence applications.Critical care (London, England) · 2026Review
- Improving definitions and innovations for identification and prevention of postoperative opioid-induced respiratory depression (OIRD): Proceedings of the International Consensus Conference.Journal of clinical anesthesia · 2026Article
- Serum lipidome remodeling in viral pneumonia: from pathophysiology to therapeutics.Frontiers in immunology · 2026Review
- Early Prediction of Acute Respiratory Distress Syndrome in Critically Ill Polytrauma Patients Using Balanced Random Forest ML: A Retrospective Cohort Study.Journal of clinical medicine · 2025Article
- Review
- Feedback loops in intensive care unit prognostic models: an under-recognised threat to clinical validity.The Lancet. Digital health · 2025Review
- Development and External Validation of a Detection Model to Retrospectively Identify Patients With Acute Respiratory Distress Syndrome.Critical care medicine · 2025Article
- [Acute respiratory distress syndrome-quo vadis : Innovative and individualized treatment approaches].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2025Review
- Generation of short-term follow-up chest CT images using a latent diffusion model in COVID-19.Japanese journal of radiology · 2025Article
- Comparison of artificial intelligence and logistic regression models for mortality prediction in acute respiratory distress syndrome: a systematic review and meta-analysis.Intensive care medicine experimental · 2025Review
- Novel machine learning models for the prediction of acute respiratory distress syndrome after liver transplantation.Frontiers in artificial intelligence · 2025Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimAcute respiratory distress syndrome or ARDS is an acute, severe form of respiratory failure characterised by poor oxygenation and bilateral pulmonary infiltrates. Advancements in signal processing and machine learning have led to promising solutions for classification, event detection and predictive models in the management of ARDS.
methodIn this review, we provide systematic description of different studies in the application of Machine Learning (ML) and artificial intelligence for management, prediction, and classification of ARDS. We searched the following databases: Google Scholar, PubMed, and EBSCO from 2009 to 2023. A total of 243 studies was screened, in which, 52 studies were included for review and analysis. We integrated knowledge of previous work providing the state of art and overview of explainable decision models in machine learning and have identified areas for future research.
resultsGradient boosting is the most common and successful method utilised in 12 (23.1%) of the studies. Due to limitation of data size available, neural network and its variation is used by only 8 (15.4%) studies. Whilst all studies used cross validating technique or separated database for validation, only 1 study validated the model with clinician input. Explainability methods were presented in 15 (28.8%) of studies with the most common method is feature importance which used 14 times.
conclusionFor databases of 5000 or fewer samples, extreme gradient boosting has the highest probability of success. A large, multi-region, multi centre database is required to reduce bias and take advantage of neural network method. A framework for validating with and explaining ML model to clinicians involved in the management of ARDS would be very helpful for development and deployment of the ML model.
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What Socratic 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 Socratic graph.