In clinical decision making for serious but rare events, there has been discussion about how to use predictive models as tools in decision making.
One example is in decision making for assessment and treatment in people at risk of suicide.
In the previous post, loss functions where considered in the context of estimating measures of central tendency for distributions. In this post, I want to look at the computation of loss functions in situations that might arise in a clinical predictive model.
For a while, I’ve been thinking about the deployment of predictive algorithms in clinical decision support. Specifically, about the difference between what we understand about a model’s performance from the publication describing it and how this might be less informative when deployed.