Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection (PASC) and Influenza Treatment System With Machine Learning
NCT ID: NCT06052527
Last Updated: 2023-12-29
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
27 participants
OBSERVATIONAL
2023-06-16
2023-10-01
Brief Summary
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Detailed Description
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The objectives of this study are:
1. To compare the classifications made by our machine learning system with those by physicians to assess the model's reliability and accuracy;
2. To evaluate Covid-19-related hospitalizations or deaths from any cause through day 28;
3. To determine if the machine learning system's recommended prescription alleviates symptoms of Covid-19, Post-Acute Sequelae of SARS-CoV-2 infection, and influenza;
4. To monitor participants who tested positive for the Covid-19 for 28 days after initiating treatment, looking for potential rebound cases.
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.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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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
Autonomous Treatment System for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza Based on Machine Learning
A novel treatment recommendation system for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza, which is based on machine learning
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.
Autonomous Treatment System for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza Based on Machine Learning
A novel treatment recommendation system for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza, which is based on machine learning
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.
Autonomous Treatment System for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza Based on Machine Learning
A novel treatment recommendation system for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza, which is based on machine learning
Interventions
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Autonomous Treatment System for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza Based on Machine Learning
A novel treatment recommendation system for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza, which is based on machine learning
Eligibility Criteria
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Inclusion Criteria
* Subjects with any high-risk conditions
* Subjects with positive sars-cov-2 rapid antigen results in 30 days
* Subjects with post Covid-19 syndrome
Exclusion Criteria
* subjects with known histories of allergic reactions to medical herbs commonly used in Traditional Chinese Medicine (TCMs)
14 Years
95 Years
ALL
No
Sponsors
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Sheng'ai Traditional Chinese Medicine Hospital
UNKNOWN
Lizora LLC
INDUSTRY
Responsible Party
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Principal Investigators
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jiale xian, MHA
Role: STUDY_CHAIR
Lizora LLC
Locations
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Sheng'Ai Traditional Medicine Hospital
Kunming, Yunnan, China
Countries
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Other Identifiers
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Liz2023Covid19
Identifier Type: -
Identifier Source: org_study_id