danielquinn
Joined 6 years ago
Joined 6 years ago
But I was thinking a little more macroscopically. We don't have any device that can see antibodies or anything as obvious in real time. Instead, we have to collect samples and test them separately in controlled environments while we look for specific markers we know to be signs of infection.
What ML does well though is see patterns in data we don't. Rather than find specific markers, I was thinking we could feed a series of different values into a model to see if a pattern emerged. We can't observe the virus directly, but we can measure the percentage makeup of different chemicals in someone's breath and see if there's a pattern in those we know to be infected vs those we know aren't -- the training set would be the pile of tests already conducted. Then we can apply that model to new data and test the results.
A breathalyzer might be just as good a comparison and is more reusable: if we can detect & capture breath/spit from enough infected & uninfected, surely a pattern would begin to present itself that could be used to diagnose, no?
Current testing is very specific, yes -- so would this system. But where current tests look for something specific: RNA or specific antibodies, this would simply look for "is this sample sufficiently like an infected case or an uninfected one?" Presumably there's more to what's going on in an infected person's lungs than just the existence of the antibodies. Could not ML help find these patterns and identify them as markers of infection?