A novel machine learning model to predict respiratory failure and invasive mechanical ventilation in critically ill patients suffering from COVID-19
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A novel machine learning model to predict respiratory failure and invasive mechanical ventilation in critically ill patients suffering from COVID-19
Authors
Keywords
-
Journal
Scientific Reports
Volume 12, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-06-23
DOI
10.1038/s41598-022-14758-x
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Performance of the ROX index to predict intubation in immunocompromised patients receiving high-flow nasal cannula for acute respiratory failure
- (2021) Virginie Lemiale et al. Annals of Intensive Care
- A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation
- (2021) Siavash Bolourani et al. JOURNAL OF MEDICAL INTERNET RESEARCH
- Uncertainty-Aware Variational-Recurrent Imputation Network for Clinical Time Series
- (2021) Ahmad Wisnu Mulyadi et al. IEEE Transactions on Cybernetics
- Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
- (2020) Chaolin Huang et al. LANCET
- ICU and Ventilator Mortality Among Critically Ill Adults With Coronavirus Disease 2019
- (2020) Sara C. Auld et al. CRITICAL CARE MEDICINE
- The Feature Selection Effect on Missing Value Imputation of Medical Datasets
- (2020) Chia-Hui Liu et al. Applied Sciences-Basel
- Directed acyclic graphs and causal thinking in clinical risk prediction modeling
- (2020) Marco Piccininni et al. BMC Medical Research Methodology
- Association of Noninvasive Oxygenation Strategies With All-Cause Mortality in Adults With Acute Hypoxemic Respiratory Failure
- (2020) Bruno L. Ferreyro et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Imbalance-XGBoost: leveraging weighted and focal losses for binary label-imbalanced classification with XGBoost
- (2020) Chen Wang et al. PATTERN RECOGNITION LETTERS
- Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial
- (2020) Hoyt Burdick et al. COMPUTERS IN BIOLOGY AND MEDICINE
- High-flow nasal cannula in COVID-19: Outcomes of application and examination of the ROX index to predict success
- (2020) Abhimanyu Chandel et al. Respiratory Care
- Machine learning for patient risk stratification for acute respiratory distress syndrome
- (2019) Daniel Zeiberg et al. PLoS One
- Factors predicting failure of noninvasive ventilation assist for preventing reintubation among medical critically ill patients
- (2017) Preecha Thomrongpairoj et al. JOURNAL OF CRITICAL CARE
- Assessment of heart rate, acidosis, consciousness, oxygenation, and respiratory rate to predict noninvasive ventilation failure in hypoxemic patients
- (2016) Jun Duan et al. INTENSIVE CARE MEDICINE
- MIMIC-III, a freely accessible critical care database
- (2016) Alistair E.W. Johnson et al. Scientific Data
- Failure of high-flow nasal cannula therapy may delay intubation and increase mortality
- (2015) Byung Ju Kang et al. INTENSIVE CARE MEDICINE
- High-Flow Oxygen through Nasal Cannula in Acute Hypoxemic Respiratory Failure
- (2015) Jean-Pierre Frat et al. NEW ENGLAND JOURNAL OF MEDICINE
- Non-invasive ventilation in community-acquired pneumonia and severe acute respiratory failure
- (2012) Andres Carrillo et al. INTENSIVE CARE MEDICINE
- Oxygen Saturations Less than 92% Are Associated with Major Adverse Events in Outpatients with Pneumonia: A Population-Based Cohort Study
- (2010) Sumit R. Majumdar et al. CLINICAL INFECTIOUS DISEASES
- A working guide to boosted regression trees
- (2008) J. Elith et al. JOURNAL OF ANIMAL ECOLOGY
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started