Easy in theory, but not in practice. For good reasons there are very strong privacy protections in place for medical records, and significant administrative barriers. And this is not even getting into the technical / infrastructure challenges.
Maybe feasible for a VC funded company with several million dollars and >20 FTEs. Less so for an academic lab with a few grad students and postdocs being paid with pocket lint.
My main concerns with the imbalance are undersampling of the negative class data distribution relative to the positive class, and overestimating performance on the test splits. I can buy that you may want to train on a balanced dataset, but the testing condition should reflect the true case distribution as closely as possible.
I agree that you would not want to use only the class priors for prediction. However, I do not think it is clear that you would want to throw that information out. Also not sure that I agree with the statement that neural network has “no memory” of the prior class distribution. That is a strong claim to make about something as opaque as a neural net model.
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.
Maybe feasible for a VC funded company with several million dollars and >20 FTEs. Less so for an academic lab with a few grad students and postdocs being paid with pocket lint.