{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,13]],"date-time":"2025-09-13T15:44:18Z","timestamp":1757778258560},"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":[[2022,7]]},"abstract":"<jats:p>We address the problem of predicting when a disease will develop, i.e., medical event time (MET), from a patient's electronic health record (EHR).\n\nThe MET of non-communicable diseases like diabetes is highly correlated to cumulative health conditions, more specifically, how much time the patient spent with specific health conditions in the past.\n\nThe common time-series representation is indirect in extracting such information from EHR because it focuses on detailed dependencies between values in successive observations, not cumulative information.\n\nWe propose a novel data representation for EHR called cumulative stay-time representation (CTR), which directly models such cumulative health conditions.\n\nWe derive a trainable construction of CTR based on neural networks that has the flexibility to fit the target data and scalability to handle high-dimensional EHR.\n\nNumerical experiments using synthetic and real-world datasets demonstrate that CTR alone achieves a high prediction performance, and it enhances the performance of existing models when combined with them.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/536","type":"proceedings-article","created":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T02:55:56Z","timestamp":1657940156000},"page":"3861-3867","source":"Crossref","is-referenced-by-count":3,"title":["Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction"],"prefix":"10.24963","author":[{"given":"Takayuki","family":"Katsuki","sequence":"first","affiliation":[{"name":"IBM Research - Tokyo"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kohei","family":"Miyaguchi","sequence":"additional","affiliation":[{"name":"IBM Research - Tokyo"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akira","family":"Koseki","sequence":"additional","affiliation":[{"name":"IBM Research - Tokyo"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Toshiya","family":"Iwamori","sequence":"additional","affiliation":[{"name":"IBM Research - Tokyo"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryosuke","family":"Yanagiya","sequence":"additional","affiliation":[{"name":"Fujita Health University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Atsushi","family":"Suzuki","sequence":"additional","affiliation":[{"name":"Fujita Health University School of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2022","name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","start":{"date-parts":[[2022,7,23]]},"theme":"Artificial Intelligence","location":"Vienna, Austria","end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:10:14Z","timestamp":1658142614000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/536"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/536","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}