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Predicting Duration of Mechanical Ventilation in Acute Respiratory Distress Syndrome Using Supervised Machine Learning
2021
Journal of Clinical Medicine
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Acute respiratory distress syndrome (ARDS) is an intense inflammatory process of the lungs. Most ARDS patients require mechanical ventilation (MV). ...
We aimed at characterizing the best early scenario during the first two days in the intensive care unit (ICU) to predict MV duration after ARDS onset using supervised machine learning (ML) approaches. ...
Conflicts of Interest: The authors declare no conflict of interest in relation to this manuscript. ...
doi:10.3390/jcm10173824
pmid:34501270
fatcat:pw3ebaf6wfat3bd3nopglkkhey
Can Big Data and Machine Learning Improve Our Understanding of Acute Respiratory Distress Syndrome?
2021
Cureus
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Acute respiratory distress syndrome (ARDS) accounts for 10% of all diagnoses in the Intensive Care Unit, and about 40% of the patients succumb to the disease. ...
The purpose of this study is to evaluate the role that big data and machine learning (ML) have played in understanding the heterogeneity of the disease and the development of various prediction algorithms ...
Introduction And Background Acute respiratory distress syndrome (ARDS) occurs in 10% of all Intensive Care Unit (ICU) patients and, unfortunately, 40% of the patients with ARDS die [1] . ...
doi:10.7759/cureus.13529
pmid:33786236
pmcid:PMC7996475
fatcat:zvxaqf4c2rhm3eid3xtcfcjyee
Machine Learning Methods to Predict Acute Respiratory Failure and Acute Respiratory Distress Syndrome
2020
Frontiers in Big Data
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, and to demonstrate the challenges and potential solutions for ARF prediction that can improve patient outcomes. ...
These phenotypes make acute respiratory failure a continuum of syndromes, rather than one homogenous disease process. ...
The authors used a random forest approach to predict development of ARDS using baseline Predictive model for acute respiratory distress syndrome events in ICU patients in china using machine learning algorithms ...
doi:10.3389/fdata.2020.579774
pmid:33693419
pmcid:PMC7931901
fatcat:5umladtmb5b23kncs5ea3vtiqi
Supervised Machine Learning for the Early Prediction of Acute Respiratory Distress Syndrome (ARDS)
[article]
2020
medRxiv
pre-print
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Purpose: Acute respiratory distress syndrome (ARDS) is a serious respiratory condition with high mortality and associated morbidity. ...
Conclusion: Supervised machine learning predictions may help predict patients with ARDS up to 48 hours prior to onset. ...
INTRODUCTION Acute respiratory distress syndrome (ARDS) is a clinical syndrome characterized by hypoxemia in the presence of non-cardiogenic pulmonary edema, and is associated with severe inflammation. ...
doi:10.1101/2020.03.19.20038364
fatcat:vd232odanbasnn25w7ibnakc44
Diagnosis and Management of Acute Respiratory Distress Syndrome in a Time of COVID-19
2020
Diagnostics
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Acute respiratory distress syndrome (ARDS) remains a serious illness with significant morbidity and mortality, characterized by hypoxemic respiratory failure most commonly due to pneumonia, sepsis, and ...
Future improved clinical outcomes in ARDS of all causes depends upon individual patient physiological and biological endotyping in order to improve accuracy and timeliness of diagnosis as well as optimal ...
A significant care gap remains around targeted medical therapies, but precision medicine approaches to RCTs and clinical management hold promise for improved clinical management and patient-relevant outcomes ...
doi:10.3390/diagnostics10121053
pmid:33291238
fatcat:7tbu3hydkfcebcjuhny2jbiqna
Use of Machine Learning to Screen for Acute Respiratory Distress Syndrome Using Raw Ventilator Waveform Data
2021
Critical Care Explorations
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Use of machine learning and physiologic information derived from raw ventilator waveform data may enable acute respiratory distress syndrome screening at early time points after intubation. ...
This approach, combined with traditional diagnostic criteria, could improve timely acute respiratory distress syndrome recognition and enable automated clinical decision support, especially in settings ...
Our Two-Step Acute Respiratory Distress Syndrome Classification
Methodology
% Acute Respiratory
Distress Syndrome
Votes in First 24 hr
Sensitivity
Specificity
Positive
Predictive Value
Negative ...
doi:10.1097/cce.0000000000000313
pmid:33458681
pmcid:PMC7803688
fatcat:3mlxx6wqjvdtfostnutuin35qe
A Computable Phenotype for Acute Respiratory Distress Syndrome Using Natural Language Processing and Machine Learning
2018
AMIA Annual Symposium Proceedings
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Acute Respiratory Distress Syndrome (ARDS) is a syndrome of respiratory failure that may be identified using text from radiology reports. ...
Linguistic pre-processing of reports was performed and text features were inputs to machine learning classifiers tuned using 10-fold cross-validation on 80% of the sample size and tested in the remaining ...
Introduction Acute respiratory distress syndrome (ARDS) is a common manifestation of pulmonary organ failure and a syndrome with profound hypoxemia with a period prevalence of 10% in all intensive care ...
pmid:30815053
pmcid:PMC6371271
fatcat:zjdldyfhxnbnvgc57sasqe7j74
Prediction model for patients with acute respiratory distress syndrome: use of a genetic algorithm to develop a neural network model
2019
PeerJ
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an acute respiratory distress syndrome Clinical Trials Network. ...
Acute respiratory distress syndrome (ARDS) is associated with significantly increased risk of death, and early risk stratification may help to choose the appropriate treatment. ...
Further prospective studies are needed to evaluate the effectiveness of the prediction model in improving clinical outcomes. ...
doi:10.7717/peerj.7719
pmid:31576250
pmcid:PMC6752189
fatcat:wapnw3tb3rfejl6pko4hoevbpe
Machine learning for patient risk stratification for acute respiratory distress syndrome
2019
PLoS ONE
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Existing prediction models for acute respiratory distress syndrome (ARDS) require manual chart abstraction and have only fair performance-limiting their suitability for driving clinical interventions. ...
We sought to develop a machine learning approach for the prediction of ARDS that (a) leverages electronic health record (EHR) data, (b) is fully automated, and (c) can be applied at clinically relevant ...
a1111111111
Introduction Acute Respiratory Distress Syndrome (ARDS) is a common and devastating critical illness, developing in 23% of patients receiving invasive mechanical ventilation, and with a ...
doi:10.1371/journal.pone.0214465
pmid:30921400
pmcid:PMC6438573
fatcat:ujymr6pf7bfgpmmd3uawvdrwem
A quantitative approach for the analysis of clinician recognition of acute respiratory distress syndrome using electronic health record data
2019
PLoS ONE
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Despite its efficacy, low tidal volume ventilation (LTVV) remains severely underutilized for patients with acute respiratory distress syndrome (ARDS). ...
We propose a computational method that addresses some of the limitations of the current approaches to automated measurement of whether ARDS is recognized by physicians. ...
[1, 2] The use of low tidal volume ventilation (LTVV) for the treatment of acute respiratory distress syndrome (ARDS) is a prime example. ...
doi:10.1371/journal.pone.0222826
pmid:31539417
pmcid:PMC6754155
fatcat:jstxzw4o35aenaf5tg7ljmhujq
Impact of Using Simulation on Critical Care Nursing Students' Knowledge and Skills of Acute Respiratory Distress Syndrome
2016
IOSR Journal of Nursing and Health Science
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Acute respiratory distress syndrome is common in critically ill patients admitted to intensive care units. ...
Conclusion: The level of critical care nursing students' knowledge and clinical performance of Acute Respiratory Distress Syndrome were general improved after application of teaching program with simulation ...
Critical care nursing students' skills regarding ARDS: The use of simulation as a teaching strategy in assessment and nursing care of acute respiratory distress syndrome can contribute to patient safety ...
doi:10.9790/1959-0505042842
fatcat:y6lmxedefrhrvjn7omxojddbrm
Metabolic and Nutrition Support in the Chronic Critical Illness Syndrome
2012
Respiratory care
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Ideally, IMS should be under the supervision of a metabolic support consultative team. Further research specifically focused on the CCI population is needed to validate this current approach. ...
Metabolic interventions are extrapolated from clinical critical care research, scientific theory, and years of CCI patient care experience. ...
eicosapentaenoic acid (EPA), ␥-linolenic acid (GLA), and antioxidants in patients with acute respiratory distress syndrome (ARDS) or acute lung injury (ALI). 102, 103 In contrast, the recently published ...
doi:10.4187/respcare.01620
pmid:22663970
fatcat:gbqec6jisrd4no373e45wlb7om
Using Machine Learning to Predict ICU Transfer in Hospitalized COVID-19 Patients
2020
Journal of Clinical Medicine
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Conclusions: A ML-based prediction model can be used as a screening tool to identify patients at risk of imminent ICU transfer within 24 h. ...
Objectives: Approximately 20–30% of patients with COVID-19 require hospitalization, and 5–12% may require critical care in an intensive care unit (ICU). ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/jcm9061668
pmid:32492874
pmcid:PMC7356638
fatcat:7fcvebe2ufg3jou3ddnpb76gse
Guillan–Barré syndrome affects the quality of life after discharge from the ICU
2009
Critical Care
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with acute lung injury and acute respiratory distress syndrome. ...
for the acute respiratory distress syndrome have been proposed. ...
acute respiratory failure. ...
doi:10.1186/cc7859
pmcid:PMC4085457
fatcat:ig2pqppnxzadnapoo3c5mq3sda
A comparison of three alveolar recruitment maneuver approaches in acute lung injury and acute respiratory distress syndrome
2009
Critical Care
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with acute lung injury and acute respiratory distress syndrome. ...
for the acute respiratory distress syndrome have been proposed. ...
acute respiratory failure. ...
doi:10.1186/cc7846
pmcid:PMC4085444
fatcat:q3brhbpdunap7lrs6risifr6ca
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