What I worry about with the undersampling are the “difficult” cases such as other types of respiratory conditions and infections. How many COPD, rhinitis, chronic bronchitis, etc patients were there in the training data? It is precisely these patients the algorithm needs to perform well on as they are higher risk and / or likely to be most prevalent among the people who seek out this app.
I think the other big question is what advantages / disadvantages does this have compared to a questionnaire administered to someone who is experiencing symptoms of an upper respiratory infection?
That being said, this study is a significant academic achievement. The authors should be very proud of what they have done. There are real challenges to doing something like this that impose hard limitations and they did as well as anyone could without infinite resources.
What I worry about with the undersampling are the “difficult” cases such as other types of respiratory conditions and infections. How many COPD, rhinitis, chronic bronchitis, etc patients were there in the training data? It is precisely these patients the algorithm needs to perform well on as they are higher risk and / or likely to be most prevalent among the people who seek out this app.
I think the other big question is what advantages / disadvantages does this have compared to a questionnaire administered to someone who is experiencing symptoms of an upper respiratory infection?
That being said, this study is a significant academic achievement. The authors should be very proud of what they have done. There are real challenges to doing something like this that impose hard limitations and they did as well as anyone could without infinite resources.