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Development and Validation of a Deep Learning Model for Prediction of Severe Outcomes in Suspected COVID-19 Infection [article]

Varun Buch, Aoxiao Zhong, Xiang Li, Marcio Aloisio Bezerra Cavalcanti Rockenbach, Dufan Wu, Hui Ren, Jiahui Guan, Andrew Liteplo, Sayon Dutta, Ittai Dayan, Quanzheng Li
2021 arXiv   pre-print
Predictive risk scores for COVID-19 severe outcomes ("CO-RISK" score) were derived from model output and evaluated on the testing dataset, as well as compared to human performance.  ...  Finally, electronic health record (EHR) data from a total of 11060 COVID-19 confirmed or suspected patients were used in this study.  ...  with the objective of developing and validating a predictive model for severe COVID-19 outcomes.  ... 
arXiv:2103.11269v2 fatcat:mrd74ptiwzau5aiatoroleirzm

Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal

Laure Wynants, Ben Van Calster, Marc M J Bonten, Gary S Collins, Thomas P A Debray, Maarten De Vos, Maria C Haller, Georg Heinze, Karel G M Moons, Richard D Riley, Ewoud Schuit, Luc J M Smits (+4 others)
2020 The BMJ (British Medical Journal)  
Study selection Studies that developed or validated a multivariable covid-19 related prediction model.  ...  Objective To review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of  ...  0.87) 12 ; and one to diagnose covid-19 by using deep learning of genomic sequences (estimated C index 0.98). 35 A further study was developed to diagnose severe disease in paediatric inpatients with  ... 
doi:10.1136/bmj.m1328 pmid:32265220 pmcid:PMC7222643 fatcat:pfqwl63yxjbe3nvkb7l5tbxkrm

Machine learning models for image-based diagnosis and prognosis of COVID-19: A systematic review (Preprint)

Mahdieh Montazeri, Roxana ZahediNasab, Ali Farahani, Hadis Mohseni, Fahimeh Ghasemian
2020 JMIR Medical Informatics  
The review identified 4 prognosis models for calculating prediction of disease severity and estimation of confinement time, for individual patients, and 41 diagnosis models for detecting COVID-19 from  ...  Machine learning models for diagnosis and prognosis of COVID-19 showed excellent discriminative performance approximately.  ...  to assist clinicians to identify COVID-19-infected patients by CT images - Segmentation early diagnosis and longitudinal follow-up of suspected pneumonia 22k(unclear) Deep learning - unclear High  ... 
doi:10.2196/25181 pmid:33735095 fatcat:jdztginrrbap3bywkgm75bwt2m

Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection [article]

Laure Wynants, Ben Van Calster, Marc MJ Bonten, Gary S Collins, Thomas PA Debray, Maarten De Vos, Maria C Haller, Georg Heinze, Karel GM Moons, Richard D Riley, Ewoud Schuit, Luc Smits (+4 others)
2020 medRxiv   pre-print
in symptomatic individuals (seven of which were deep learning models for COVID-19 diagnosis utilising computed tomography (CT) results); and seven prognostic models for predicting mortality risk, or length  ...  We identified three models to predict hospital admission from pneumonia and other events (as a proxy for covid-19 pneumonia) in the general population; nine diagnostic models to detect COVID-19 infection  ...  -We identified ten prognostic models for COVID-19 infected patients, of which six aimed to predict mortality risk in confirmed or suspected COVID-19 patients, two aimed to predict progression to a severe  ... 
doi:10.1101/2020.03.24.20041020 fatcat:criftpi4dndbfnd2d54l6ckpky

Automatic Detection of Coronavirus (COVID-19) from Chest CT Images using VGG16-Based Deep-Learning

Abolfazl Karimiyan Abdar, Seyyed Mostafa Sadjadi, Hamid Soltanian-Zadeh, Ali Bashirgonbadi, Mehran Naghibi
2020 2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME)  
In this paper, we present a deep learning-based Convolutional Neural Network (CNN) model we developed for the classification of COVID-19 positive patients from healthy subjects using chest CT.  ...  We used 10979 chest CT images of 131 patients with COVID-19 and 150 healthy subjects for training, validating, and testing of the proposed model.  ...  In [8] , a multitask deep learning model was developed for jointly identifying COVID-19 patients and segmenting infections from chest CT images.  ... 
doi:10.1109/icbme51989.2020.9319326 fatcat:kph25tqzczgcvnlnjcafzhh6v4

COVID ‐19 diagnosis system by deep learning approaches

Hemanta Kumar Bhuyan, Chinmay Chakraborty, Yogesh Shelke, Suvendu Kumar Pani
2021 Expert systems  
For mass segmentation of the infected region, a deep Convolutional Neural Network (CNN) is used to identify the specific infected area and classify it into COVID-19 or Non-COVID-19 patients with a full-resolution  ...  In this paper, regional deep learning approaches are used to detect infected areas by the lungs' coronavirus.  ...  Initially, it collected different suspected and infected COVID-19 patient cases for training databases.  ... 
doi:10.1111/exsy.12776 pmid:34511691 pmcid:PMC8420221 fatcat:rnhifvjjzbd45ihlwdm73en26e

Applications of Artificial Intelligence and Machine Learning in Diagnosis and Prognosis of COVID-19 infection: A systematic review

Mahdieh Montazeri, Ali Afraz, Mitra Montazeri, Sadegh Nejatzadeh, Fatemeh Rahimi, Mohsen Taherian, Mohadeseh Montazeri, Leila Ahmadian
2021 Frontiers in Health Informatics  
Sharing data and experiences for the development, validation, and updating of COVID-19 related prediction models is needed.  ...  Our aim in this study was to summarize information on the use of intelligent models for predicting and diagnosing the Coronavirus disease 2019 (COVID-19) to help early and timely diagnosis of the disease.Material  ...  Sharing data and experiences for the development, validation, and updating of COVID-19 related prediction models is needed.  ... 
doi:10.30699/fhi.v10i1.321 fatcat:lqo6ffcoynfhbeotdmfzunybne

Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning

Wanshan Ning, Shijun Lei, Jingjing Yang, Yukun Cao, Peiran Jiang, Qianqian Yang, Jiao Zhang, Xiaobei Wang, Fenghua Chen, Zhi Geng, Liang Xiong, Hongmei Zhou (+6 others)
2020 Nature Biomedical Engineering  
We show the utility of the database for prediction of COVID-19 morbidity and mortality outcomes using a deep learning algorithm trained with data from 1,170 patients and 19,685 manually labelled CT slices  ...  AbstractData from patients with coronavirus disease 2019 (COVID-19) are essential for guiding clinical decision making, for furthering the understanding of this viral disease, and for diagnostic modelling  ...  Development of HUST-19 for predicting clinical outcomes of COVID-19 patients.  ... 
doi:10.1038/s41551-020-00633-5 pmid:33208927 fatcat:5txwilbg55hozmjeronh7cpefy

Diagnosis/Prognosis of COVID-19 Images: Challenges, Opportunities, and Applications [article]

Arash Mohammadi, Yingxu Wang, Nastaran Enshaei, Parnian Afshar, Farnoosh Naderkhani, Anastasia Oikonomou, Moezedin Javad Rafiee, Helder C. R. Oliveira, Svetlana Yanushkevich, Konstantinos N. Plataniotis
2020 arXiv   pre-print
Signal Processing (SP) and Deep Learning (DL) models can assist in development of robust Radiomics solutions for diagnosis/prognosis, severity assessment, treatment response, and monitoring of COVID-19  ...  SL/DL-based Radiomic models specific to the analysis of COVID-19 infection are then described covering the following four domains: Segmentation of COVID-19 lesions; Predictive models for outcome prediction  ...  We would like to thank the consulting committee and EiC of IEEE SPM for their two-round reviews and encouraging comments.  ... 
arXiv:2012.14106v1 fatcat:5c52xdd3qneblfsjchtqwigzvu

Artificial Intelligence-Driven Assessment of Radiological Images for COVID-19

Yassine Bouchareb, Pegah Moradi Khaniabadi, Faiza Al Kindi, Humoud Al Dhuhli, Isaac Shiri, Habib Zaidi, Arman Rahmim
2021 Computers in Biology and Medicine  
Artificial Intelligence (AI) methods have significant potential for diagnosis and prognosis of COVID-19 infections.  ...  and validation as appropriate for AI-based COVID-19 studies.  ...  Acknowledgements This work was supported by the Omani Research Council Grant, grant number RC/COVID-MED/RADI/20/01.  ... 
doi:10.1016/j.compbiomed.2021.104665 pmid:34343890 pmcid:PMC8291996 fatcat:nstm5p56rbgf5pd6hei42iia34

COVED: A Hardware Accelerated Soft Computing Enabled Intelligent Value Chain Based Diagnostic Automation for nCOVID-19 Estimation and Identification

Swarnava Biswas, Debajit Sen, Dinesh Bhatia, Moumita Mukherjee, Department of Physics, School of Basic and Applied Sciences, Adamas University, Kolkata, West Bengal, India
2021 International Journal of Statistics in Medical Research  
Results: Over the HRCT image dataset, the developed deep ensemble model is collated to different state-of-the-art transfer learning (TL) models.  ...  Purpose: COVID-19, a global pandemic, first appeared in the city of Wuhan, China, and has since spread differently across geographical borders, classes, and genders from various age groups, sometimes mutating  ...  ACKNOWLEDGEMENTS The authors also acknowledge The Neotia University and Adamas University for providing excellent research infrastructure and for necessary funding.  ... 
doi:10.6000/1929-6029.2021.10.14 fatcat:42ewzz2v5zekzgu3nwrog3d6mu

A web-based Diagnostic Tool for COVID-19 Using Machine Learning on Chest Radiographs (CXR) [article]

Evariste Bosco Gueguim Kana, Martiale Gaetan Zebaze Kana, Armand F. Donfack Kana, Roussel Hardo Azanfack Kenfack
2020 medRxiv   pre-print
This paper reports the development and web deployment of an inference model for Coronavirus COVID-19 using machine vision on chest radiographs (CXR).  ...  The performance metrics showed an accuracy of 99%, a recall valued of 99.8%, a precision of 99% and an F1 score of 99.8% for COVID-19 inference.  ...  There is a growing interest in the use of CXR and CT scans for the screening, diagnosis and management of patients with suspected or known COVID-19 infection.  ... 
doi:10.1101/2020.04.21.20063263 fatcat:xohjvu6ppnenlbmk7po2fngubi

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

Maria Elena Laino, Angela Ammirabile, Alessandro Posa, Pierandrea Cancian, Sherif Shalaby, Victor Savevski, Emanuele Neri
2021 Diagnostics  
Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection.  ...  As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/diagnostics11081317 fatcat:prsewme7s5hojmb4rzk2sfgwa4

COVID-19 prediction using AI analytics for South Korea

Adwitiya Sinha, Megha Rathi
2021 Applied intelligence (Boston)  
The severe spread of the COVID-19 pandemic has created a situation of public health emergency and global awareness.  ...  Analysis delineates key points in the outbreak spreading, indicating that the models driven by machine intelligence and deep learning can be effective in providing a quantitative view of the epidemical  ...  A deep learning framework is developed to accurately predict COVID-19 patients in another novel work [17] . Using CT images AUC value obtained from the model was 0.96.  ... 
doi:10.1007/s10489-021-02352-z pmid:34764592 pmcid:PMC8027716 fatcat:5uiu65qzb5d6dl57frchog3hoi

Modelling and Simulation of COVID-19 Outbreak Prediction Using Supervised Machine Learning

Rachid Zagrouba, Muhammad Adnan Khan, Atta-ur-Rahman, Muhammad Aamer Saleem, Muhammad Faheem Mushtaq, Abdur Rehman, Muhammad Farhan Khan
2021 Computers Materials & Continua  
This article proposes a predictive framework incorporating Support Vector Machines (SVM) in the forecasting of a potential outbreak of COVID-19.  ...  The proposed SVM system model exhibits 98.88% and 96.79% result in terms of accuracy during training and validation respectively.  ...  Funding Statement: The author(s) received no specific funding for this study.  ... 
doi:10.32604/cmc.2021.014042 fatcat:lhy5qrmbs5ghdal4zd3oabai3a
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