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The aim of the study is to develop a prognostic prediction model based on machine learning algorithms in patients affected by coronavirus disease 2019 (COVID-19), the prediction model will be capable to recognize patient with favorable prognosis or patient with poor prognosis by intelligent systems data analysis.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| training cohort | data collection | ||
| validation cohort | data collection |
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| Measure | Description | Time Frame |
|---|---|---|
| COVID-19 clinical course | Data about sex, age, symptoms start date, symptoms, comorbidity, vital parameters, hematochemical blood tests, therapy, oxygen support, radiology, clinical disease progression will be collected. The collected data will be analyzed through a machine learning based approach to predict the prognosis of patients affected by COVID-19. | 2 months |
| Measure | Description | Time Frame |
|---|---|---|
| Application of machine learning algorithms on data of patients affected by COVID-19 | The collected data will be analyzed through a machine learning based approach to establish correlations between collected data and the prognosis of patients affected by COVID-19. | 2 months |
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Inclusion Criteria:
Exclusion Criteria:
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Italian caucasian patients aged over 18 years old SARS-CoV-2 infection confirmed by PCR
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of L'Aquila | Recruiting | L’Aquila | 67100 | Italy |
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| ID | Term |
|---|---|
| D000086382 | COVID-19 |
| ID | Term |
|---|---|
| D011024 | Pneumonia, Viral |
| D011014 | Pneumonia |
| D012141 | Respiratory Tract Infections |
| D007239 | Infections |
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| D014777 |
| Virus Diseases |
| D018352 | Coronavirus Infections |
| D003333 | Coronaviridae Infections |
| D030341 | Nidovirales Infections |
| D012327 | RNA Virus Infections |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |