A deep learning method for predicting the COVID-19 ICU patient outcome fusing X-rays, respiratory sounds, and ICU parameters

[1]  S. Lemieux,et al.  Deep learning of chest X-rays can predict mechanical ventilation outcome in ICU-admitted COVID-19 patients , 2022, Scientific reports.

[2]  C. Lin,et al.  COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data , 2022, European Radiology.

[3]  A. Katsaggelos,et al.  CoCross: An ICT Platform Enabling Monitoring Recording and Fusion of Clinical Information Chest Sounds and Imaging of COVID-19 ICU Patients , 2022, Healthcare.

[4]  C. Salvarani,et al.  VECTOR: An algorithm for the detection of COVID-19 pneumonia from velcro-like lung sounds , 2022, Computers in Biology and Medicine.

[5]  P. Prasanna,et al.  Predicting Mechanical Ventilation and Mortality in COVID-19 Using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study , 2021, Diagnostics.

[6]  M. Rashid,et al.  Early Prediction of COVID-19 Ventilation Requirement and Mortality from Routinely Collected Baseline Chest Radiographs, Laboratory, and Clinical Data with Machine Learning , 2021, Journal of multidisciplinary healthcare.

[7]  Nicos Maglaveras,et al.  Detection of squawks in respiratory sounds of mechanically ventilated COVID-19 patients , 2021, 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

[8]  D. Colombi,et al.  Prediction of 28-day mortality in critically ill patients with COVID-19: Development and internal validation of a clinical prediction model , 2021, PloS one.

[9]  Unais Sait,et al.  A deep-learning based multimodal system for Covid-19 diagnosis using breathing sounds and chest X-ray images , 2021, Applied Soft Computing.

[10]  Farah E. Shamout,et al.  An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department , 2021, npj Digital Medicine.

[11]  Hossein Aboutalebi,et al.  COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images , 2021, Diagnostics.

[12]  T. Cook,et al.  Mortality in patients admitted to intensive care with COVID‐19: an updated systematic review and meta‐analysis of observational studies , 2021, Anaesthesia.

[13]  D. King,et al.  Multisystem outcomes and predictors of mortality in critically ill patients with COVID-19: Demographics and disease acuity matter more than comorbidities or treatment modalities , 2021, The journal of trauma and acute care surgery.

[14]  Joelle Pineau,et al.  COVID-19 Deterioration Prediction via Self-Supervised Representation Learning and Multi-Image Prediction. , 2021, ArXiv.

[15]  Rui Pedro Paiva,et al.  Automatic Classification of Adventitious Respiratory Sounds: A (Un)Solved Problem? † , 2020, Sensors.

[16]  Mark Finkelstein,et al.  Combining Initial Radiographs and Clinical Variables Improves Deep Learning Prognostication in Patients with COVID-19 from the Emergency Department , 2020, Radiology. Artificial intelligence.

[17]  A. Katsaggelos,et al.  DeepCOVID-XR: An Artificial Intelligence Algorithm to Detect COVID-19 on Chest Radiographs Trained and Tested on a Large U.S. Clinical Data Set , 2020, Radiology.

[18]  T. Saraya,et al.  Evidence of the Sequential Changes of Lung Sounds in COVID-19 Pneumonia Using a Novel Wireless Stethoscope with the Telemedicine System , 2020, Internal medicine.

[19]  P. Póvoa,et al.  Clinical course and outcomes of critically ill patients with COVID-19 infection: a systematic review , 2020, Clinical Microbiology and Infection.

[20]  T. Flanigan,et al.  Predictors of COVID‐19 severity: A literature review , 2020, Reviews in medical virology.

[21]  C. Aldecoa,et al.  Patient characteristics, clinical course and factors associated to ICU mortality in critically ill patients infected with SARS-CoV-2 in Spain: A prospective, cohort, multicentre study☆ , 2020, Revista Española de Anestesiología y Reanimación (English Edition).

[22]  S. Tabik,et al.  COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images , 2020, IEEE Journal of Biomedical and Health Informatics.

[23]  Yoshua Bengio,et al.  Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning , 2020, Cureus.

[24]  Koichi Yuki,et al.  COVID-19 pathophysiology: A review , 2020, Clinical Immunology.

[25]  Jiang Xie,et al.  Association Between Hypoxemia and Mortality in Patients With COVID-19 , 2020, Mayo Clinic Proceedings.

[26]  Richard M. Leahy,et al.  Brain Lesion Detection Using A Robust Variational Autoencoder and Transfer Learning , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[27]  Ali Narin,et al.  Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks , 2020, Pattern Analysis and Applications.

[28]  N. Xiong,et al.  Characteristics of Pulmonary Auscultation in Patients with 2019 Novel Coronavirus in China , 2020, Respiration.

[29]  T. Velavan,et al.  The COVID‐19 epidemic , 2020, Tropical medicine & international health : TM & IH.

[30]  Ioanna Chouvarda,et al.  Supporting integrated care with a flexible data management framework built upon Linked Data, HL7 FHIR and ontologies , 2019, J. Biomed. Informatics.

[31]  G. Washko,et al.  Imaging Patterns are Associated with Interstitial Lung Abnormality Progression and Mortality. , 2019, American journal of respiratory and critical care medicine.

[32]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[34]  Stefan Jaeger,et al.  Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. , 2014, Quantitative imaging in medicine and surgery.

[35]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[36]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[37]  Petri Toiviainen,et al.  MIR in Matlab (II): A Toolbox for Musical Feature Extraction from Audio , 2007, ISMIR.

[38]  K. Doi,et al.  Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules. , 2000, AJR. American journal of roentgenology.

[39]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .