Learning predictive checklists from continuous medical data

TL;DR

This paper introduces a method to automatically learn predictive checklists from continuous medical data using mixed-integer programming, improving sepsis prediction accuracy over existing explainable models.

Contribution

It extends existing checklist learning methods to handle continuous data with a mixed-integer programming approach, enhancing clinical decision support tools.

Findings

Outperforms explainable machine learning baselines in sepsis prediction

Effective handling of continuous medical data

Improved interpretability of predictive models

Abstract

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.