The developers sampled obvious cases og hypotension and nonhypotension, and trained the model to distinguish those. And also validated it on data that was similarly dichotomous. In reality the outcome is often between these two scenarios.
But worse, they also introduce a more severe problem where as range of an important predictor is only available in the hypotension outcome.
Indeed, the confusion here is (I think) because your first comment
> Sorry for asking, but how is this relevant to the article?
Sounds accusatory.
Please don’t respond to this with a question.
Going back the path of arguments to common ground if you will. It works quite well in my experience if you’re interested in genuine discussion.
PS: how something “sounds” is really difficult to say in a written medium. It might say more about the reader than the writer.
No, it’s not difficult. And not it’s not the reader. When multiple readers all agree about the same interpretation of the writer. It might have been unintentional on the part of the writer, but that doesn’t make it “difficult” Or the readers fault.
Random open ended discussion can be good, but I bet it's wise to assume tht most random musings arent really as interesting as you might think.
In any case thanks for clarifying.
The problem would, however, have been clear, if the model was compared to simply using the current mean blood pressure (MAP) as a predictor of hypotension, because MAP is the problematic predictor variable. Instead, the model was only compared to short-term changes in MAP (ΔMAP), which is obviously nonsensical and has an AUROC of ~0.55.
It's sort of inevitable in such a non-teleological process. As in each step in it serves its own purpose, and so the whole thing doesn't really serve the purpose that we like to assume for it - ie. give us great thoughtful inventions. That's why it took so long to stop the Theranos train, that's why it takes so fucking long to roll out polyvalent vaccines (ie. all-in-one vaccines), and so on. (I'm picking on medtech here but there are many others, the Boeing + FAA MCAS fuckup, the absolute limpdick paralysis of nuclear power - it needed a combination of half the world on fire + prelude-to-WWIII to get it moving again, and so on.)
https://doi.org/10.1097/ALN.0000000000004320
Basically, the training and validation data was engineered so an important range for one of the predictor variables was only present in one of the outcomes, making perfect prediction possible for these cases.
I summarize the paper in this Twitter thread: https://twitter.com/JohsEnevoldsen/status/156164115389992960...