AI application to distinguish between COVID-19 and flu based on disease symptoms

This is also the first work to study seasonal flu in the context of the COVID-19 pandemic.

Specifically, Professor Farrokh Alemi and his colleagues assessed the possibility that the patient had COVID-19 or the flu or another respiratory illness before conducting the test.

He said: “In the event of limited COVID-19 testing, or delayed test results, clinicians, especially those living in impacted communities, will have to rely on signs and symptoms rather than waiting for confirmed lab results.Our algorithms can help health authorities identify and categorize for more effective care of patients, while they are still waiting for the test results from the lab or giving them priority for testing in the event of a shortage of test kits.”.

The scientists’ algorithm was built based on the different symptoms of patients of different ages and genders. The results of the study suggest that health authorities should monitor for different signs and symptoms to diagnose COVID-19 depending on the specific time of year.

For example, if it’s not flu season, a fever could be considered a symptom of COVID-19. However, during flu season, a person with a cough is more likely to get the flu than to get COVID-19. Research has also shown that it is incorrect to attribute all fever cases during flu season to COVID. In addition, it is more important and accurate to diagnose COVID-19 based on clusters of symptoms than on individual symptoms.

The algorithms were created by analyzing symptoms recorded from 774 COVID-19 patients in China and 273 COVID-19 patients in the US, and compared them with pathological information from 2,885 cases. influenza and 884 cases of influenza-like respiratory illness in the United States.

Because these algorithms are very complex, which can make it difficult for doctors to treat patients, the scientists created a computer based on artificial intelligence (AI) for diagnostic purposes only. From there, doctors can make triage decisions about how to care for their patients while they await official lab results.

Professor Alemi said: “When there is a new outbreak, data collection takes a lot of time. The ability to quickly analyze this data can help reduce the time it takes to differentiate between illnesses with similar symptoms. The method mentioned in this report is very useful for quick response should the next pandemic occur.”.

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