{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T03:03:24Z","timestamp":1783134204998,"version":"3.54.6"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2023\/265","type":"proceedings-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:31:30Z","timestamp":1691742690000},"page":"2388-2396","source":"Crossref","is-referenced-by-count":1,"title":["Hierarchical Apprenticeship Learning for Disease Progression Modeling"],"prefix":"10.24963","author":[{"given":"Xi","family":"Yang","sequence":"first","affiliation":[{"name":"IBM Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ge","family":"Gao","sequence":"additional","affiliation":[{"name":"North Carolina State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Chi","sequence":"additional","affiliation":[{"name":"North Carolina State University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}","theme":"Artificial Intelligence","location":"Macau, SAR China","acronym":"IJCAI-2023","number":"32","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2023,8,19]]},"end":{"date-parts":[[2023,8,25]]}},"container-title":["Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:43:24Z","timestamp":1691743404000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2023\/265"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2023\/265","relation":{},"subject":[],"published":{"date-parts":[[2023,8]]}}}