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Prediction and Feature Importance Analysis for Severity of COVID-19 in South Korea Using Artificial Intelligence: Model Development and Validation
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1001/jama.2020.2648openalex-snapshot
Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China
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The value of clinical parameters in predicting the severity of COVID‐19
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Abnormal lung quantification in chest CT images of COVID‐19 patients with deep learning and its application to severity prediction
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A framework for sensitivity analysis of decision trees
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Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study
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Truncated inception net: COVID-19 outbreak screening using chest X-rays
inferredcites →https://doi.org/10.1016/j.cmi.2020.04.012openalex-snapshot
Risk factors for disease severity, unimprovement, and mortality in COVID-19 patients in Wuhan, China
inferredcites →https://doi.org/10.1016/j.jaci.2020.04.006openalex-snapshot
Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan
inferredcites →https://doi.org/10.1016/j.procs.2020.09.258openalex-snapshot
Machine learning for coronavirus covid-19 detection from chest x-rays
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/10.1016/s0140-6736(20)30211-7openalex-snapshot
Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study
inferredcites →https://doi.org/10.1038/s41467-020-17280-8openalex-snapshot
Early triage of critically ill COVID-19 patients using deep learning
inferredcites →https://doi.org/10.1038/s41598-020-75767-2openalex-snapshot
Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study
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/nejm196803212781204openalex-snapshot
Medical Records That Guide and Teach
inferredcites →https://doi.org/10.1109/access.2020.3003810openalex-snapshot
Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-Rays
inferredcites →https://doi.org/10.1111/j.2517-6161.1958.tb00292.xopenalex-snapshot
The Regression Analysis of Binary Sequences
inferredcites →https://doi.org/10.1145/238061.238163openalex-snapshot
Game theory, on-line prediction and boosting
inferredcites →https://doi.org/10.1186/s12879-020-05128-xopenalex-snapshot
Epidemiological and clinical features of 2019 novel coronavirus diseases (COVID-19) in the South of Iran
inferredcites →https://doi.org/10.1371/journal.pone.0236621openalex-snapshot
Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs
inferredcites →https://doi.org/10.15585/mmwr.mm6949e2openalex-snapshot
Summary of Guidance for Public Health Strategies to Address High Levels of Community Transmission of SARS-CoV-2 and Related Deaths, December 2020
inferredcites →https://doi.org/10.1613/jair.953openalex-snapshot
SMOTE: Synthetic Minority Over-sampling Technique
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/10.2196/21801openalex-snapshot
Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing
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/10.3389/fcell.2020.00683openalex-snapshot
Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests
inferredcites →https://doi.org/10.3389/fnbot.2013.00021openalex-snapshot
Gradient boosting machines, a tutorial
inferredcites →https://doi.org/10.3389/fpubh.2020.587937openalex-snapshot
Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods
inferredcites →https://doi.org/10.3760/cma.j.issn.0254-6450.2020.02.003openalex-snapshot
[The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China].
inferredcites →https://doi.org/10.48550/arxiv.2003.04655openalex-snapshot
Lung Infection Quantification of COVID-19 in CT Images with Deep Learning
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Machine Learning–Based Prediction of COVID-19 Mortality With Limited Attributes to Expedite Patient Prognosis and Triage: Retrospective Observational Study
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inferred← citeshttps://doi.org/10.3389/fnut.2023.1165854openalex-snapshot
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