An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

@article{Shamout2021AnAI,
  title={An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department},
  author={Farah E. Shamout and Yiqiu Shen and Nan Wu and Aakash Kaku and Jungkyu Park and Taro Makino and Stanislaw Jastrzkebski and Duo Wang and Ben-Bin Zhang and Siddhant Dogra and Meng Cao and Narges Razavian and David Kudlowitz and Lea Azour and William H Moore and Yvonne W. Lui and Yindalon Aphinyanaphongs and Carlos Fernandez-Granda and Krzysztof J. Geras},
  journal={npj Digital Medicine},
  year={2021},
  volume={4},
  pages={1-11}
}
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating… Expand
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References

SHOWING 1-10 OF 98 REFERENCES
Early triage of critically ill COVID-19 patients using deep learning
TLDR
A deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission and is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness. Expand
Machine learning to assist clinical decision-making during the COVID-19 pandemic
TLDR
This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume. Expand
Deep Interpretable Early Warning System for the Detection of Clinical Deterioration
TLDR
The ‘Deep Early Warning System’ (DEWS) is proposed, an interpretable end-to-end deep learning model that interpolates temporal data and predicts the probability of an adverse event, defined as the composite outcome of cardiac arrest, mortality or unplanned ICU admission. Expand
A Fully Automatic Deep Learning System for COVID-19 Diagnostic and Prognostic Analysis
TLDR
Deep learning provides a convenient tool for fast screening COVID-19 and finding potential high-risk patients, which may be helpful for medical resource optimization and early prevention before patients show severe symptoms. Expand
Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography
TLDR
Using a large computed Tomography database from 4,154 patients, an AI system is developed that can diagnose NCP and differentiate it from other common pneumonia and normal controls and is made available globally to assist the clinicians to combat COVID-19. Expand
Automated detection of COVID-19 cases using deep neural networks with X-ray images
TLDR
A new model for automatic COVID-19 detection using raw chest X-ray images is presented and can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients. Expand
Prospective and External Evaluation of a Machine Learning Model to Predict In-Hospital Mortality of Adults at Time of Admission.
TLDR
Prospectively and multisite retrospective evaluations of a machine learning model demonstrated good discrimination of in-hospital mortality for adult patients at the time of admission, and the model was implementable at a system level. Expand
Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT
TLDR
A deep learning model can accurately detect COVID-19 and differentiate it from community acquired pneumonia and other lung diseases. Expand
Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks
TLDR
A convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (−ve) and extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate. Expand
Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays
TLDR
Experimental analysis on 6,523 chest X-rays belonging to different institutions demonstrated the effectiveness of the proposed approach, with an average time for COVID-19 detection of approximately 2.5 seconds and an average accuracy equal to 0.97. Expand
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5
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