Prediction models that are used to guide medical decisions are usually regulated under medical device regulation. This means, putting a calculator out there to promote the use your new prediction model is likely to break some rules.

The lasso works really well in particular settings and for particular purposes. If you are after high prediction performance alone and you have a rather large sample size, it can be an excellent choice indeed. But most analytical goals are not only about prediction

This is right tho. Let’s therefore call them sensitivity positive predictive value curves bsky.app/profile/laur...

For details: arxiv.org/abs/2412.10288

TL;DR: Explainable AI models often don't do a good job explaining. They can be very useful for description. We should be really careful when using Explainable AI in clinical decision making, and even when judging face validity of AI models

Excellently led by @alcarriero.bsky.social

This is, however, not clever or safe writing, it is a bad collective habit that needs to stop. Not by avoiding references to causality but by clear referencing to it

pubmed.ncbi.nlm.nih.gov/37286459/

And taking this analogy one step further: it gives genuine phone repair shops a bad name

Clients: “I want to find real, meaningful clusters”
Me: “I want world peace, which is more likely to happen than what you want”

In medicine they are called "risk factors" and, of course, you want all "important" risk factors in your model all the time

Unless a risk factor is not statistically significant then you can drop that factor without issues

Also, the fact that a model with the best AUC doesn't always mean the model makes the best predictions is lost in such cases too