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During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making.
Analysis Of Survival Data , vol. 21 (CRC Press, Boca Raton, 1984)
Cox, D. R. & Oakes, D · 1984
Earlier work this paper cites.
An introduction to the bootstrap (CRC press, 1994)
Efron, B. & Tibshirani, R. J · 1994
Earlier work this paper cites.
Ensemble methods in machine learning
Dietterich, T. G · 2000
Earlier work this paper cites.
Monte Carlo cross validation
Xu, Q. & Liang, Y · 2001
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
Deng, J. et al · 2009
Earlier work this paper cites.
A survey on transfer learning
Pan, S. J. & Yang, Q · 2009
Earlier work this paper cites.
Survival Analysis , vol. 66 (John Wiley & Sons, New York, 2011)
Miller Jr, R. G · 2011
Earlier work this paper cites.
Random search for hyper-parameter optimization
Bergstra, J. & Bengio, Y · 2012
Earlier work this paper cites.
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y. & Lipson, H · 2014
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous distributed systems
Martín, A. et al · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. & Ba, J · 2015
Earlier work this paper cites.
Delving deep into rectifiers: surpassing human-level performance on ImageNet classification
He, K., Zhang, X., Ren, S. & Sun, J · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S. & Sun, J · 2016
Earlier work this paper cites.
National early warning score (news) 2: Standardising the assessment of acute-illness severity in the nhs. report of a working party. https://www.rcplondon.ac.uk/projects/outputs/national-early-warning-score-news-2 (2017)
Royal College of Physicians · 2017
Earlier work this paper cites.
Lightgbm: A highly efficient gradient boosting decision tree
Ke, G. et al · 2017
Earlier work this paper cites.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R. et al · 2017
Earlier work this paper cites.
Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L. & Weinberger, K. Q · 2017
Earlier work this paper cites.
CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning
Rajpurkar, P. et al · 2017
Earlier work this paper cites.
ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Wang, X. et al · 2017
Earlier work this paper cites.
High-resolution breast cancer screening with multi-view deep convolutional neural networks
Geras, K. J. et al · 2017
Earlier work this paper cites.
Interpretable machine learning in healthcare
Ahmad, M. A., Eckert, C. & Teredesai, A · 2018
Earlier work this paper cites.
Sanity checks for saliency maps
Adebayo, J. et al · 2018
Earlier work this paper cites.
Attention-based deep multiple instance learning
Ilse, M., Tomczak, J. M. & Welling, M · 2018
Earlier work this paper cites.
A scalable discrete-time survival model for neural networks
Gensheimer, M. F. & Narasimhan, B · 2018
Cited alongside, same era.
Cox-nnet: an artificial neural network method for prognosis prediction of high-throughput omics data
Ching, T., Zhu, X. & Garmire, L. X · 2018
Cited alongside, same era.
DeepSurv: personalized treatment recommender system using a cox proportional hazards deep neural network
Katzman, J. L. et al · 2018
Cited alongside, same era.
Emergency department and hospital crowding: causes, consequences, and cures
McKenna, P. et al · 2019
Cited alongside, same era.
Deep interpretable early warning system for the detection of clinical deterioration
Shamout, F. E., Zhu, T., Sharma, P., Watkinson, P. J. & Clifton, D. A · 2019
Cited alongside, same era.
Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest ct
Li, L. et al · 2020
Closest in time.
Automated detection of COVID-19 cases using deep neural networks with X-ray images
Ozturk, T. et al · 2020
Closest in time.
A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis
Wang, S. et al · 2020
Closest in time.
Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography
Zhang, K. et al · 2020
Closest in time.
Classification of COVID-19 patients from chest ct images using multi-objective differential evolution–based convolutional neural networks
Singh, D., Kumar, V. & Kaur, M · 2020
Closest in time.
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Globally-aware multiple instance classifier for breast cancer screening
Shen, Y. et al · 2019
Cited alongside, same era.
Challenges in the deployment and operation of machine learning in practice
Baier, L., Jöhren, F. & Seebacher, S · 2019
Cited alongside, same era.
Exploring the active mechanism of berberine against hcc by systematic pharmacology and experimental validation
Song, L. et al · 2019
Cited alongside, same era.
A novel approach for multi-label chest X-ray classification of common thorax diseases
Allaouzi, I. & Ahmed, M. B · 2019
Cited alongside, same era.
Sdfn: Segmentation-based deep fusion network for thoracic disease classification in chest X-ray images
Liu, H. et al · 2019
Cited alongside, same era.
PyTorch: An imperative style, high-performance deep learning library
Paszke, A. et al · 2019
Cited alongside, same era.
Creating a COVID-19 surge clinic to offload the emergency department
Baugh, J. J. et al · 2020
Cited alongside, same era.
Prediction models for diagnosis and prognosis of COVID-19 infection: systematic review and critical appraisal
Wynants, L. et al · 2020
Closest in time.
Automated assessment of COVID-19 pulmonary disease severity on chest radiographs using convolutional siamese neural networks
Li, M. D. et al · 2020
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COVID-19 outbreak in italy: experimental chest X-ray scoring system for quantifying and monitoring disease progression
Borghesi, A. & Maroldi, R · 2020
Closest in time.
Clinical and chest radiography features determine patient outcomes in young and middle age adults with COVID-19
Toussie, D. et al · 2020
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Clinical decision support systems for triage in the emergency department using intelligent systems: a review
Fernandes, M. et al · 2020
Closest in time.
Shen, Y. et al · 2020
Closest in time.
Classifier-agnostic saliency map extraction
Żołna, K., Geras, K. J. & Cho, K · 2020
Closest in time.
Narin, A., Kaya, C. & Pamuk, Z · 2020
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Machine learning for clinical outcome prediction
Shamout, F. E., Zhu, T. & Clifton, D. A · 2020
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Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays
Brunese, L., Mercaldo, F., Reginelli, A. & Santone, A · 2020
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Generalizability of deep learning tuberculosis classifier to COVID-19 chest radiographs: New tricks for an old algorithm?
Paul, H. Y., Kim, T. K. & Lin, C. T · 2020
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Prospective and external evaluation of a machine learning model to predict in-hospital mortality of adults at time of admission
Brajer, N. et al · 2020
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Venous and arterial thromboembolic complications in COVID-19 patients admitted to an academic hospital in Milan, Italy
Lodigiani, C. et al · 2020
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Large-vessel stroke as a presenting feature of COVID-19 in the young
Oxley, T. J. et al · 2020
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Kawasaki-like disease: emerging complication during the COVID-19 pandemic
Viner, R. M. & Whittaker, E · 2020
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Multi-label chest X-ray image classification via category-wise residual attention learning
Guan, Q. & Huang, Y · 2020
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Early triage of critically ill COVID-19 patients using deep learning
Liang, W. et al · 2020
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Clinical features of COVID-19 in elderly patients: A comparison with young and middle-aged patients
Liu, K., Chen, Y., Lin, R. & Han, K · 2020
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