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| Name | Class |
|---|---|
| Max-Planck-Institute Tuebingen | OTHER |
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The aim of this study is to use artificial intelligence in the form of machine learning analysing vital signs as well as symptoms of patients suffering from Covid19 to identify predictors of disease progression and severe course of disease.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Training cohort | Randomly selection of 80% of the study population. The machine learning algorithm is trained on this dataset |
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| Validation cohort | Randomly selection of 20% of the study population. The machine learning algorithm which was trained on the basis of the training data cohort is validated on the validation cohort. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Machine learning | Other | Machine learning on vital parameters, clinical symptoms and underlying diseases |
|
| Measure | Description | Time Frame |
|---|---|---|
| Probability of Participants for Hospitalisation or Fatal Outcome | Detection of severe acute respiratory syndrome- Corona Virus 2 (SARS-CoV2) to recovery, hospitalisation or fatal outcome up to 5 weeks |
| Measure | Description | Time Frame |
|---|---|---|
| Probability of Participants for Intensive Care Unit Admission | Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks | |
| Probability of Participants for Fatal Outcome | Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks |
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Inclusion Criteria:
Exclusion Criteria:
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Patients with detection of SARS-CoV2
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Annika Buchholz, PhD | Contact | +49 151 51819576 | annika.buchholz@tuebingen.mpg.de |
| Name | Affiliation | Role |
|---|---|---|
| Bernhard Schoelkopf, PhD | Max-Planck-Institute, Tuebingen, Germany | Study Chair |
| Juergen Hetzel, MD | University Hospital of Tuebingen, Tuebingen, Germany | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University Hospital of Tuebingen | Recruiting | Tübingen | 72076 | Germany |
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| ID | Term |
|---|---|
| D000086382 | COVID-19 |
| D018450 | Disease Progression |
| ID | Term |
|---|---|
| D011024 | Pneumonia, Viral |
| D011014 | Pneumonia |
| D012141 | Respiratory Tract Infections |
| D007239 | Infections |
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| ID | Term |
|---|---|
| D000069550 | Machine Learning |
| ID | Term |
|---|---|
| D001185 | Artificial Intelligence |
| D000465 | Algorithms |
| D055641 | Mathematical Concepts |
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| Machine based evaluation | Other | Quantification of the prediction power and identification of the most relevant predictive parameters |
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| Prediction of persisting health impairment by using standardized questionnaires | Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks |
| Detection of symptoms, vital parameters and comorbidities predicting clinical course | Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks |
| Influence of size of training data set | Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks |
| Influence of viral load on the course of disease/ clinical outcome | Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks |
| Influence of different virus variants on the course of disease/ clinical outcome | Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks |
| Influence of SARS-CoV2 vaccination (yes/no) on the course of disease/ clinical outcome | Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks |
| Evaluation of parameters (symptoms, vital parameters, comorbidities) according to their potential of clinical course predictions | Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks |
| Probability of Participants for hospitalisation | Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks |
| Influence of different SARS-CoV2 vaccines on the course of disease/ clinical outcome | Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks |
| D014777 |
| Virus Diseases |
| D018352 | Coronavirus Infections |
| D003333 | Coronaviridae Infections |
| D030341 | Nidovirales Infections |
| D012327 | RNA Virus Infections |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |