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| Name | Class |
|---|---|
| Sheng'ai Traditional Chinese Medicine Hospital | UNKNOWN |
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This is an open-tabled, one-arm observatory trial to assess the effectiveness and safety of the Autonomous Treatment System Based on Machine Learning in patients with Covid-19, Post-Acute Sequelae of SARS-CoV-2 infection and influenza.
This study has enrolled 27 patients diagnosed with Covid-19, Post-Acute Sequelae of SARS-CoV-2 infection, and influenza. Of these patients, 26 are outpatients, and 1 is hospitalized. After screening based on the inclusion and exclusion criteria, eligible patients will receive prescriptions recommended by the Autonomous Treatment System Based on Machine Learning in this observational trial.
The objectives of this study are:
Participants will use an online application to receive the recommended prescription results and will forward these results to a physician for verification. Patients are instructed to complete the online analysis every 3 days or whenever their symptoms change, whichever comes first. They are also asked to adhere to the prescribed medication regimen. Research physicians will conduct follow-ups with patients every 3 days via phone calls. The potential treatments patients may receive include any of the following Traditional Chinese Medicine formulas: LizCovidCure-1, LizCovidCure-2, LizCovidCure-3, LizCovidCure-4, and LizCovid-5.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Active Covid-19 Infection | Patients with positive SARS-CoV-2 rapid antigen test results within 60 days before the start of the study will be administered pre-defined TCM prescriptions recommended by the Autonomous Treatment System based on machine learning |
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| Post-Covid-19 Syndrome | Patients with positive Covid-19 antigen test results obtained more than 60 days before the start of the study will be administered pre-defined TCM prescriptions recommended by the Autonomous Treatment System based on machine learning. |
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| Influenza | Patients with negative SARS-CoV-2 rapid antigen test results and who are diagnosed with influenza will be administered pre-defined TCM prescriptions recommended by the Autonomous Treatment System based on machine learning. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Autonomous Treatment System for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza Based on Machine Learning | Other | A novel treatment recommendation system for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza, which is based on machine learning |
| Measure | Description | Time Frame |
|---|---|---|
| Classification Accuracy | compare the classifications made by our machine learning system with those by physicians, to assess the model's reliability | 1 Day |
| Measure | Description | Time Frame |
|---|---|---|
| Hospitalization Rate and Death | we assess Covid-19-related hospitalization or death from any cause through day 28 | 28 Days |
| Measure | Description | Time Frame |
|---|---|---|
| Symptom Alleviation | Days of symptom disappearance | 28 Days |
| Re-infection Cases | Number of cases with recurrence-infection after treatment | 28 days |
Inclusion Criteria:
Exclusion Criteria:
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Patients with active SARS-Cov-2 infection within 30 days patients with Post-Acute Sequelae of SARS-CoV-2 infection and patients with influenza.
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| Name | Affiliation | Role |
|---|---|---|
| jiale xian, MHA | Lizora LLC | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Sheng'Ai Traditional Medicine Hospital | Kunming | Yunnan | 650000 | China |
No plan to share the individual participant data (IPD).
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|
| ID | Term |
|---|---|
| D000086382 | COVID-19 |
| D000094024 | Post-Acute COVID-19 Syndrome |
| D007251 | Influenza, Human |
| ID | Term |
|---|---|
| D011024 | Pneumonia, Viral |
| D011014 | Pneumonia |
| D012141 | Respiratory Tract Infections |
| D007239 | Infections |
| D014777 | Virus Diseases |
| D018352 | Coronavirus Infections |
| D003333 | Coronaviridae Infections |
| D030341 | Nidovirales Infections |
| D012327 | RNA Virus Infections |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D000094025 | Post-Infectious Disorders |
| D002908 | Chronic Disease |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D009976 | Orthomyxoviridae Infections |
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