On the public citation graph
Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study
0 signed · 75 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s11263-019-01228-7openalex-snapshot
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
inferredcites →https://doi.org/10.1016/j.cell.2020.04.045openalex-snapshot
Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography
inferredcites →https://doi.org/10.1016/j.coviro.2018.01.001openalex-snapshot
Transmission routes of respiratory viruses among humans
inferredcites →https://doi.org/10.1016/j.ejrad.2004.01.005openalex-snapshot
CT techniques for imaging the lung: recommendations for multislice and single slice computed tomography
inferredcites →https://doi.org/10.1016/j.ejrad.2020.108961openalex-snapshot
Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT?
inferredcites →https://doi.org/10.1016/j.ipm.2009.03.002openalex-snapshot
A systematic analysis of performance measures for classification tasks
inferredcites →https://doi.org/10.1016/j.media.2019.101628openalex-snapshot
Shape and margin-aware lung nodule classification in low-dose CT images via soft activation mapping
inferredcites →https://doi.org/10.1016/j.patcog.2007.04.009openalex-snapshot
Cost-sensitive boosting for classification of imbalanced data
inferredcites →https://doi.org/10.1016/s0140-6736(20)30183-5openalex-snapshot
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
inferredcites →https://doi.org/10.1038/nm.2971openalex-snapshot
Computed tomography–based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression
inferredcites →https://doi.org/10.1038/s41592-019-0686-2openalex-snapshot
SciPy 1.0: fundamental algorithms for scientific computing in Python
inferredcites →https://doi.org/10.1038/s42256-020-0180-7openalex-snapshot
An interpretable mortality prediction model for COVID-19 patients
inferredcites →https://doi.org/10.1056/nejmoa2002032openalex-snapshot
Clinical Characteristics of Coronavirus Disease 2019 in China
inferredcites →https://doi.org/10.1056/nejmp2000929openalex-snapshot
A Novel Coronavirus Emerging in China — Key Questions for Impact Assessment
inferredcites →https://doi.org/10.1073/pnas.2009637117openalex-snapshot
Identifying airborne transmission as the dominant route for the spread of COVID-19
inferredcites →https://doi.org/10.1080/22221751.2020.1745095openalex-snapshot
Laboratory diagnosis of emerging human coronavirus infections – the state of the art
inferredcites →https://doi.org/10.1101/2020.03.25.20043331openalex-snapshot
A Machine Learning Model Reveals Older Age and Delayed Hospitalization as Predictors of Mortality in Patients with COVID-19
inferredcites →https://doi.org/10.1109/cvpr.2016.308openalex-snapshot
Rethinking the Inception Architecture for Computer Vision
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot
Densely Connected Convolutional Networks
inferredcites →https://doi.org/10.1109/iccv.2017.74openalex-snapshot
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
inferredcites →https://doi.org/10.1136/bmj.m1328openalex-snapshot
Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1148/radiol.2020201365openalex-snapshot
The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society
inferredcites →https://doi.org/10.1148/radiol.2020201433openalex-snapshot
Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia
inferredcites →https://doi.org/10.1164/rccm.201908-1581stopenalex-snapshot
Diagnosis and Treatment of Adults with Community-acquired Pneumonia. An Official Clinical Practice Guideline of the American Thoracic Society and Infectious Diseases Society of America
inferredcites →https://doi.org/10.12688/f1000research.7035.2openalex-snapshot
RNA-Seq workflow: gene-level exploratory analysis and differential expression
inferredcites →https://doi.org/10.1364/oe.18.015256openalex-snapshot
An open-source toolkit for the volumetric measurement of CT lung lesions
inferredcites →https://doi.org/10.1371/journal.pmed.1001940openalex-snapshot
Correction: World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseases, 2010: A Data Synthesis
inferredcites →https://doi.org/10.1371/journal.pone.0230548openalex-snapshot
Association of radiologic findings with mortality of patients infected with 2019 novel coronavirus in Wuhan, China
inferredcites →https://doi.org/10.17863/cam.64134openalex-snapshot
SciPy 1.0: fundamental algorithms for scientific computing in Python
inferredcites →https://doi.org/10.21037/atm-20-3026openalex-snapshot
Machine learning-based CT radiomics method for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: a multicenter study
inferredcites →https://doi.org/10.2139/ssrn.3562456openalex-snapshot
Development and External Validation of a Prognostic Multivariable Model on Admission for Hospitalized Patients with COVID-19
inferredcites →https://doi.org/10.2196/19569openalex-snapshot
COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation
inferredcites →https://doi.org/10.2196/24018openalex-snapshot
Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation
inferredcites →https://doi.org/10.7150/thno.45985openalex-snapshot
CT quantification of pneumonia lesions in early days predicts progression to severe illness in a cohort of COVID-19 patients
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.5192446openalex-extracted
Redefining Experimental, Empirical, and Exploratory Research in AI Era
inferred← citeshttps://doi.org/10.1007/978-3-031-15063-0_32openalex-snapshot
Safety Helmet Detection at Construction Sites Using YOLOv5 and YOLOR
inferred← citeshttps://doi.org/10.1007/978-981-95-6770-6_128openalex-snapshot
Image Segmentation of Road-Surface Cracks Using U-Net
inferred← citeshttps://doi.org/10.1007/978-981-96-3912-0_12openalex-snapshot
Fast Evaluation of Crack Propagation Using Artificial Neural Network
inferred← citeshttps://doi.org/10.1007/978-981-97-1972-3_38openalex-snapshot
Evaluation of Displacement of an L-shaped Concrete Specimen using Recurrent Neural Networks
inferred← citeshttps://doi.org/10.1007/978-981-99-7434-4_128openalex-snapshot
Prediction of Liquified Soil Settlement Based on Artificial Neural Network
inferred← citeshttps://doi.org/10.1007/s00011-024-01852-1openalex-snapshot
CCL18, CHI3L1, ANG2, IL-6 systemic levels are associated with the extent of lung damage and radiomic features in SARS-CoV-2 infection
inferred← citeshttps://doi.org/10.1007/s10140-021-02008-yopenalex-snapshot
Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence
inferred← citeshttps://doi.org/10.1007/s10278-022-00691-yopenalex-snapshot
Deep Learning–Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs
inferred← citeshttps://doi.org/10.1016/j.inffus.2025.103535openalex-snapshot
Multi-period prediction of residual lung lesions in COVID-19 survivors with multi-head temporal constrained network
inferred← citeshttps://doi.org/10.1016/j.seps.2024.102054openalex-snapshot
An innovative patient clustering method using data envelopment Analysis–Discriminant analysis and artificial neural networks: A case study in healthcare systems
inferred← citeshttps://doi.org/10.1038/s41598-022-07890-1openalex-snapshot
A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data
inferred← citeshttps://doi.org/10.1038/s41598-023-45532-2openalex-snapshot
Augmentation strategies for an imbalanced learning problem on a novel COVID-19 severity dataset
inferred← citeshttps://doi.org/10.1093/jamia/ocab098openalex-snapshot
The application of artificial intelligence and data integration in COVID-19 studies: a scoping review
inferred← citeshttps://doi.org/10.1109/icsc64553.2025.10968183openalex-snapshot
Progressive Use of IoT and 5G Network-Based Systems Post COVID-19 Pandemic
inferred← citeshttps://doi.org/10.1111/acem.14447openalex-snapshot
Prediction models for severe manifestations and mortality due to COVID ‐19: A systematic review
inferred← citeshttps://doi.org/10.1136/bmj.m1328openalex-snapshot
Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal
inferred← citeshttps://doi.org/10.1186/s12911-023-02344-8openalex-snapshot
Visual transformer and deep CNN prediction of high-risk COVID-19 infected patients using fusion of CT images and clinical data
inferred← citeshttps://doi.org/10.14201/adcaij.28715openalex-snapshot
A Novel Study for Automatic Two-Class and Three-Class COVID-19 Severity Classification of CT Images using Eight Different CNNs and Pipeline Algorithm
inferred← citeshttps://doi.org/10.21007/etd.cghs.2021.0552openalex-snapshot
Responding to the Pandemic: A Multicountry Study on Social-Political Factors and Health Outcomes of COVID-19
inferred← citeshttps://doi.org/10.2196/46340openalex-snapshot
Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis
inferred← citeshttps://doi.org/10.3389/fmed.2021.689568openalex-snapshot
CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study
inferred← citeshttps://doi.org/10.3389/fphys.2023.1288246openalex-snapshot
Computed tomography-based imaging biomarker identifies coal workers’ pneumoconiosis
inferred← citeshttps://doi.org/10.3390/app112311229openalex-snapshot
Application of Various YOLO Models for Computer Vision-Based Real-Time Pothole Detection
inferred← citeshttps://doi.org/10.3390/diagnostics11081317openalex-snapshot
The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review
inferred← citeshttps://doi.org/10.3390/diagnostics13142358openalex-snapshot
Mixed-Input Deep Learning Approach to Sleep/Wake State Classification by Using EEG Signals
inferred← citeshttps://doi.org/10.3390/diagnostics15070845openalex-snapshot
Classification of the ICU Admission for COVID-19 Patients with Transfer Learning Models Using Chest X-Ray Images
inferred← citeshttps://doi.org/10.3390/electronics12183878openalex-snapshot
Machine and Deep Learning Algorithms for COVID-19 Mortality Prediction Using Clinical and Radiomic Features
inferred← citeshttps://doi.org/10.3390/jcm10091961openalex-snapshot
A State-of-the-Art Survey on Artificial Intelligence to Fight COVID-19
inferred← citeshttps://doi.org/10.3390/jpm11100955openalex-snapshot
Graphical Trajectory Comparison to Identify Errors in Data of COVID-19: A Cross-Country Analysis
inferred← citeshttps://doi.org/10.3390/jpm12040535openalex-snapshot
Determination of the Severity and Percentage of COVID-19 Infection through a Hierarchical Deep Learning System
inferred← citeshttps://doi.org/10.3390/s22020461openalex-snapshot
Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model
inferred← citeshttps://doi.org/10.4108/eetpht.10.5613openalex-snapshot
Influenza Diagnosis Deep Learning: Machine Learning Approach for Pharyngeal Image Infection
inferred← citeshttps://doi.org/10.55905/rcssv14n3-001openalex-snapshot
Rede de ajuste dinâmico de imagens de tomografia computadorizada para predição da severidade da COVID-19
inferred← citeshttps://doi.org/10.61186/ijbc.15.3.93openalex-snapshot
Toward artificial intelligence (AI) applications in the determination of COVID-19 infection severity: considering AI as a disease control strategy in future pandemics
inferred← citeshttps://doi.org/10.64818/pijet.3107.8486.0004openalex-snapshot
Redefining Experimental, Empirical, and Exploratory Research in AI Era
inferred← citeshttps://doi.org/10.64898/2026.08.20.26360948openalex-snapshot
Development of a Deep Learning Model for Opportunistic Screening of Osteoporosis using Chest Radiographs
inferred← citeshttps://doi.org/10.7759/cureus.18768openalex-snapshot
Accuracy of Machine Learning Models to Predict Mortality in COVID-19 Infection Using the Clinical and Laboratory Data at the Time of Admission
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.