A comprehensive review of machine learning used to combat COVID-19
Coronavirus disease (COVID-19) has had a significant impact on global health since the
start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed …
start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed …
[HTML][HTML] Trends in the application of deep learning networks in medical image analysis: Evolution between 2012 and 2020
L Wang, H Wang, Y Huang, B Yan, Z Chang… - European journal of …, 2022 - Elsevier
Purpose To evaluate the general rules and future trajectories of deep learning (DL) networks
in medical image analysis through bibliometric and hot spot analysis of original articles …
in medical image analysis through bibliometric and hot spot analysis of original articles …
Are we overdoing it? Changes in diagnostic imaging workload during the years 2010–2020 including the impact of the SARS-CoV-2 pandemic
M Winder, AJ Owczarek, J Chudek, J Pilch-Kowalczyk… - Healthcare, 2021 - mdpi.com
Since the 1990s, there has been a significant increase in the number of imaging
examinations as well as a related increase in the healthcare expenditure and the exposure …
examinations as well as a related increase in the healthcare expenditure and the exposure …
Deep learning-based lesion subty** and prediction of clinical outcomes in COVID-19 pneumonia using chest CT
The main objective of this work is to develop and evaluate an artificial intelligence system
based on deep learning capable of automatically identifying, quantifying, and characterizing …
based on deep learning capable of automatically identifying, quantifying, and characterizing …
Biomarkers of severe COVID-19 pneumonia on admission using data-mining powered by common laboratory blood tests-datasets
In the epidemiological COVID-19 research, artificial intelligence is a unique approach to
make predictions about disease severity to manage COVID-19 patients. A limitation of …
make predictions about disease severity to manage COVID-19 patients. A limitation of …
Multimodal graph attention network for COVID-19 outcome prediction
When dealing with a newly emerging disease such as COVID-19, the impact of patient-and
disease-specific factors (eg, body weight or known co-morbidities) on the immediate course …
disease-specific factors (eg, body weight or known co-morbidities) on the immediate course …
Multimodal data fusion using sparse canonical correlation analysis and cooperative learning: a COVID-19 cohort study
Through technological innovations, patient cohorts can be examined from multiple views
with high-dimensional, multiscale biomedical data to classify clinical phenotypes and predict …
with high-dimensional, multiscale biomedical data to classify clinical phenotypes and predict …
A robust COVID-19 mortality prediction calculator based on lymphocyte count, urea, C-reactive protein, age and sex (LUCAS) with chest X-rays
There have been numerous risk tools developed to enable triaging of SARS-CoV-2 positive
patients with diverse levels of complexity. Here we presented a simplified risk-tool based on …
patients with diverse levels of complexity. Here we presented a simplified risk-tool based on …
ISPE‐Endorsed Guidance in Using Electronic Health Records for Comparative Effectiveness Research in COVID‐19: Opportunities and Trade‐Offs
As the scientific research community along with healthcare professionals and decision
makers around the world fight tirelessly against the coronavirus disease 2019 (COVID‐19) …
makers around the world fight tirelessly against the coronavirus disease 2019 (COVID‐19) …
Development and validation of a multimodal-based prognosis and intervention prediction model for COVID-19 patients in a multicenter cohort
The ability to accurately predict the prognosis and intervention requirements for treating
highly infectious diseases, such as COVID-19, can greatly support the effective management …
highly infectious diseases, such as COVID-19, can greatly support the effective management …