Machine Learning Tools for Acute Respiratory Distress Syndrome Detection and Prediction
F Rubulotta, S Bahrami, DC Marshall… - Critical Care …, 2024 - journals.lww.com
Abstract Machine learning (ML) tools for acute respiratory distress syndrome (ARDS)
detection and prediction are increasingly used. Therefore, understanding risks and benefits …
detection and prediction are increasingly used. Therefore, understanding risks and benefits …
[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 …
Accuracy of artificial intelligence algorithms in predicting acute respiratory distress syndrome: a systematic review and meta-analysis
Y **ong, Y Gao, Y Qi, Y Zhi, J Xu, K Wang… - BMC Medical Informatics …, 2025 - Springer
Background Acute respiratory distress syndrome (ARDS) is a serious threat to human life.
Hence, early and accurate diagnosis and treatment are crucial for patient survival. This meta …
Hence, early and accurate diagnosis and treatment are crucial for patient survival. This meta …
Automatic ARDS surveillance with chest X-ray recognition using convolutional neural networks
Objective This study aims to design, validate and assess the accuracy a deep learning
model capable of differentiation Chest X-Rays between pneumonia, acute respiratory …
model capable of differentiation Chest X-Rays between pneumonia, acute respiratory …
A Novel Method for Medical Predictive Models in Small Data Using Out-of-Distribution Data and Transfer Learning
Applying deep learning to medical research with limited data is challenging. This study
focuses on addressing this difficulty through a case study, predicting acute respiratory failure …
focuses on addressing this difficulty through a case study, predicting acute respiratory failure …
Semi-supervised KPCA-based monitoring techniques for detecting COVID-19 infection through blood tests
This study introduces a new method for identifying COVID-19 infections using blood test
data as part of an anomaly detection problem by combining the kernel principal component …
data as part of an anomaly detection problem by combining the kernel principal component …
A systematic review of machine learning models for management, prediction and classification of ARDS
Aim Acute respiratory distress syndrome or ARDS is an acute, severe form of respiratory
failure characterised by poor oxygenation and bilateral pulmonary infiltrates. Advancements …
failure characterised by poor oxygenation and bilateral pulmonary infiltrates. Advancements …
Lung Imaging and Artificial Intelligence in ARDS
D Chiumello, S Coppola, G Catozzi, F Danzo… - Journal of Clinical …, 2024 - mdpi.com
Artificial intelligence (AI) can make intelligent decisions in a manner akin to that of the
human mind. AI has the potential to improve clinical workflow, diagnosis, and prognosis …
human mind. AI has the potential to improve clinical workflow, diagnosis, and prognosis …
Machine Learning-Based Prediction Models of Acute Respiratory Failure in Patients with Acute Pesticide Poisoning
The prognosis of patients with acute pesticide poisoning depends on their acute respiratory
condition. Here, we propose machine learning models to predict acute respiratory failure in …
condition. Here, we propose machine learning models to predict acute respiratory failure in …
Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review
Temporal electronic health record (EHR) data are often preferred for clinical prediction tasks
because they offer more complete representations of a patient's pathophysiology than static …
because they offer more complete representations of a patient's pathophysiology than static …