Presentation at Machine Learning for Healthcare 2023 in New York on our work on rank-based compatibility. During the conference I presented a brief spotlight talk introducing our work and also had the chance to present a poster going into more detail. I’ve included copies of both in this blog post.
You can find a link to the post about the paper here.
A recording of the spotlight intro video.
Spotlight presentation slides
Link to download presentation.
Poster
Abstract
Updating clinical machine learning models is necessary to maintain performance, but may cause compatibility issues, affecting user-model interaction. Current compatibility measures have limitations, especially where models generate risk-based rankings. We propose a new rank-based compatibility measure and loss function that optimizes discriminative performance while promoting good compatibility. We applied this to a mortality risk stratification study using MIMIC data, resulting in more compatible models while maintaining performance. These techniques provide new approaches for updating risk stratification models in clinical settings.
