  Checklists, while being only recently introduced in the medical domain, have
become highly popular in daily clinical practice due to their combined
effectiveness and great interpretability. Checklists are usually designed by
expert clinicians that manually collect and analyze available evidence.
However, the increasing quantity of available medical data is calling for a
partially automated checklist design. Recent works have taken a step in that
direction by learning predictive checklists from categorical data. In this
work, we propose to extend this approach to accomodate learning checklists from
continuous medical data using mixed-integer programming approach. We show that
this extension outperforms a range of explainable machine learning baselines on
the prediction of sepsis from intensive care clinical trajectories.
