ReviewFrontiers in big data2020
Machine Learning Methods to Predict Acute Respiratory Failure and Acute Respiratory Distress Syndrome.
Review in Frontiers in big data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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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.
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Who cites it
14 citing papers in PubMed.
- Association of Arterial PaCODiagnostics (Basel, Switzerland) · 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
- A reinforcement learning-guided interpretable method for postoperative sepsis prediction with Hilbert-Schmidt Independence Criterion.Frontiers in big data · 2026Article
- Predicting failure of extubation and non-invasive respiratory support in critically ill patients: clinical complexity, limitations of traditional indices, and machine learning perspectives.Frontiers in medicine · 2026Review
- Predicting mortality and risk factors of sepsis related ARDS using machine learning models.Scientific reports · 2025Article
- Article
- A risk prediction model for efficient intubation in the emergency department: A 4-year single-center retrospective analysis.Journal of the American College of Emergency Physicians open · 2024Article
- Computational Simulation of Virtual Patients Reduces Dataset Bias and Improves Machine Learning-Based Detection of ARDS from Noisy Heterogeneous ICU Datasets.IEEE open journal of engineering in medicine and biology · 2024Article
- HIRA: Heart Rate Interval based Rapid Alert score to characterize autonomic dysfunction among patients with sepsis-related acute respiratory failure (ARF).Physiological measurement · 2023Article
- Long short-term memory model identifies ARDS and in-hospital mortality in both non-COVID-19 and COVID-19 cohort.BMJ health & care informatics · 2023Article
- Machine learning predicts lung recruitment in acute respiratory distress syndrome using single lung CT scan.Annals of intensive care · 2023Article
- Prediction of respiratory failure risk in patients with pneumonia in the ICU using ensemble learning models.PloS one · 2023Article
- Multitask Learning With Recurrent Neural Networks for Acute Respiratory Distress Syndrome Prediction Using Only Electronic Health Record Data: Model Development and Validation Study.JMIR medical informatics · 2022Article
- Applying artificial neural network for early detection of sepsis with intentionally preserved highly missing real-world data for simulating clinical situation.BMC medical informatics and decision making · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Acute respiratory failure (ARF) is a common problem in medicine that utilizes significant healthcare resources and is associated with high morbidity and mortality. Classification of acute respiratory failure is complicated, and it is often determined by the level of mechanical support that is required, or the discrepancy between oxygen supply and uptake. These phenotypes make acute respiratory failure a continuum of syndromes, rather than one homogenous disease process. Early recognition of the risk factors for new or worsening acute respiratory failure may prevent that process from occurring. Predictive analytical methods using machine learning leverage clinical data to provide an early warning for impending acute respiratory failure or its sequelae. The aims of this review are to summarize the current literature on ARF prediction, to describe accepted procedures and common machine learning tools for predictive tasks through the lens of ARF prediction, and to demonstrate the challenges and potential solutions for ARF prediction that can improve patient outcomes.
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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.