Hi Sam!
Well, I shouldn’t pretend to have more than a superficial understanding
of your use case, however …
Let me make a few more comments.



 shartzog:

I think my path forward will be to add in the 5th “HomeRecovery” label without excluding pneumonia cases.


I think you’re overly fixated on adding another label.  I think the
multi-label approach is your friend, and is already doing the necessary
work for you.


As for confounding factors related to cases that have not yet “concluded”, I controlled for those by only considering cases where the onset of symptoms was more than a month in the past.


So, as I understand it, in all of your samples the person has been
sick (“onset of symptoms”), and, as an approximation, has either
died or (approximately) recovered (“more than a month in the past”).
So let’s take those as the rules of the game.
Under these assumptions, is seems to me that your “HomeRecovery”
label is exactly equivalent to “Not Hospitalized” and “Not Deceased.”
Again, I think multi-label is your friend.


I’d also love to be able to capture the “Never Sick” vs. “got sick but still recovered at home” distinction, but it’s just not present in the data as far as I can figure.


Yes, if the reality of your data is that (to a good approximation) each
person has been sick, and (to a good approximation) you don’t have
any “Never Sick” samples in your data, then you won’t be able to
capture a “Never Sick” vs. “got sick” distinction.
To recap:  From what you’ve said so far, it seems to me that the four
labels you discussed originally are just the set you need to capture
all of the cases in your data.  Adding more labels – be they extraneous,
conflicting, or redundant – will, as a general rule, just muddy the waters
and make your model harder to train.
Best.
K. Frank