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The research aim to collect large samples of COVID-19 disease patients with clinical symptoms, laboratory and imaging examination data. Screening the biological indicators which are related to the occurrence of severe diseases. Then, investigators using artificial intelligence (AI) technology deep learning method to find a prediction model that can dynamically quantify COVID-19 disease severity.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Observed group | The patients who were detected COVID-19 disease by RT-PCR and CT imaging. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| other | Other | clinical diagnosis |
|
| Measure | Description | Time Frame |
|---|---|---|
| discrimination | The performance of our prediction model is evaluated with the receiver operating characteristic (ROC) curves, areas under the curves (AUCs) and concordance index (c-index). | up to 3 months |
| Calibration | The calibration curves analysis is used to show error between the predicted clinical phenotype with prediction model and actual clinical phenotype. | up to 3 months |
| Net benefit | Decision curve analysis was used to determine whether the models could be considered useful tools for clinical decisionmaking by comparing the net benefits at any threshold. | up to 3 months |
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Inclusion Criteria:
Exclusion Criteria:
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Patients of COVID-19 disease
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Xinqiao Hospital of Chongqing | Chongqing | 400000 | China |
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