Learning predictive checklists from continuous medical data
This work addresses the need for automated checklist design in healthcare, offering a method to handle continuous data for improved interpretability in medical predictions, though it is incremental as it extends prior work from categorical to continuous data.
The paper tackled the problem of learning predictive checklists from continuous medical data, proposing a mixed-integer programming approach that outperforms explainable machine learning baselines in predicting sepsis from intensive care clinical trajectories.
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.