Covid19
Conditions
Keywords
Covid19, machine learning, prediction model, convolutional neural network
Brief summary
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.
Interventions
None listed
Sponsors
University of L'Aquila
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* patients aged over 18 positive for COVID-19 by polymerase chain reaction assay for rhino-pharyngeal swab
Exclusion criteria
* Under 18 aged
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| COVID-19 clinical course | 2 months | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Application of machine learning algorithms on data of patients affected by COVID-19 | 2 months | 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. |
Countries
Italy
Outcome results
None listed