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.
Keywords
- Data Mining: DM: Mining spatial and/or temporal data
- Data Mining: DM: Applications
- Multidisciplinary Topics and Applications: MDA: Health and medicine
