[HTML][HTML] Supervised machine learning for the early prediction of acute respiratory distress syndrome (ARDS)
S Le, E Pellegrini, A Green-Saxena, C Summers… - Journal of Critical …, 2020 - Elsevier
Purpose Acute respiratory distress syndrome (ARDS) is a serious respiratory condition with
high mortality and associated morbidity. The objective of this study is to develop and …
high mortality and associated morbidity. The objective of this study is to develop and …
Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort study
XF Ding, JB Li, HY Liang, ZY Wang, TT Jiao… - Journal of translational …, 2019 - Springer
Background To develop a machine learning model for predicting acute respiratory distress
syndrome (ARDS) events through commonly available parameters, including baseline …
syndrome (ARDS) events through commonly available parameters, including baseline …
Machine learning for patient risk stratification for acute respiratory distress syndrome
Background Existing prediction models for acute respiratory distress syndrome (ARDS)
require manual chart abstraction and have only fair performance–limiting their suitability for …
require manual chart abstraction and have only fair performance–limiting their suitability for …
Predicting duration of mechanical ventilation in acute respiratory distress syndrome using supervised machine learning
Background: Acute respiratory distress syndrome (ARDS) is an intense inflammatory
process of the lungs. Most ARDS patients require mechanical ventilation (MV). Few studies …
process of the lungs. Most ARDS patients require mechanical ventilation (MV). Few studies …
[HTML][HTML] Multitask learning with recurrent neural networks for acute respiratory distress syndrome prediction using only electronic health record data: model …
Background Acute respiratory distress syndrome (ARDS) is a condition that is often
considered to have broad and subjective diagnostic criteria and is associated with …
considered to have broad and subjective diagnostic criteria and is associated with …
Machine learning classifier models can identify acute respiratory distress syndrome phenotypes using readily available clinical data
P Sinha, MM Churpek, CS Calfee - American journal of respiratory …, 2020 - atsjournals.org
Rationale: Two distinct phenotypes of acute respiratory distress syndrome (ARDS) with
differential clinical outcomes and responses to randomly assigned treatment have …
differential clinical outcomes and responses to randomly assigned treatment have …
[HTML][HTML] Mortality prediction for patients with acute respiratory distress syndrome based on machine learning: a population-based study
Background Traditional scoring systems for patients' outcome prediction in intensive care
units such as Oxygenation Saturation Index (OSI) and Oxygenation Index (OI) may not …
units such as Oxygenation Saturation Index (OSI) and Oxygenation Index (OI) may not …
Using machine learning for the early prediction of sepsis-associated ARDS in the ICU and identification of clinical phenotypes with differential responses to treatment
Y Bai, J Xia, X Huang, S Chen, Q Zhan - Frontiers in Physiology, 2022 - frontiersin.org
Background: An early diagnosis model with clinical phenotype classification is key for the
early identification and precise treatment of sepsis-associated acute respiratory distress …
early identification and precise treatment of sepsis-associated acute respiratory distress …
Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome
Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute
respiratory distress syndrome (ARDS) or acute lung injury (ALI) is a clinical syndrome that …
respiratory distress syndrome (ARDS) or acute lung injury (ALI) is a clinical syndrome that …
eARDS: A multi-center validation of an interpretable machine learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among critically ill adults …
We present an interpretable machine learning algorithm called 'eARDS'for predicting ARDS
in an ICU population comprising COVID-19 patients, up to 12-hours before satisfying the …
in an ICU population comprising COVID-19 patients, up to 12-hours before satisfying the …
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