Hierarchical Apprenticeship Learning for Disease Progression Modeling

Abstract

Disease progression modeling (DPM) plays an essential role in characterizing patients' historical pathways and predicting their future risks. Apprenticeship learning (AL) aims to induce decision-making policies by observing and imitating expert behaviors. In this paper, we investigate the incorporation of AL-derived patterns into DPM, utilizing a Time-aware Hierarchical EM Energy-based Subsequence (THEMES) AL approach. To the best of our knowledge, this is the first study incorporating AL-derived progressive and interventional patterns for DPM. We evaluate the efficacy of this approach in a challenging task of septic shock early prediction, and our results demonstrate that integrating the AL-derived patterns significantly enhances the performance of DPM.