{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T19:11:55Z","timestamp":1760728315946,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030821524"},{"type":"electronic","value":"9783030821531"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-82153-1_46","type":"book-chapter","created":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T23:26:36Z","timestamp":1628292396000},"page":"559-570","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Readmission Prediction with Knowledge Graph Attention and RNN-Based Ordinary Differential Equations"],"prefix":"10.1007","author":[{"given":"Su","family":"Pei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Niu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueping","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingni","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,7]]},"reference":[{"issue":"1","key":"46_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"S Barbieri","year":"2020","unstructured":"Barbieri, S., Kemp, J., Perez-Concha, O., Kotwal, S., Jorm, L.: Benchmarking deep learning architectures for predicting readmission to the ICU and describing patients-at-risk. Sci. Rep. 10(1), 1\u201310 (2020)","journal-title":"Sci. Rep."},{"key":"46_CR2","unstructured":"Chen, R., Rubanova, Y., Bettencourt, J., Duvenaud, D.: Neural ordinary differential equations (2018)"},{"key":"46_CR3","doi-asserted-by":"crossref","unstructured":"Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: GRAM: graph-based attention model for healthcare representation learning. In: the 23rd ACM SIGKDD International Conference (2016)","DOI":"10.1145\/3097983.3098126"},{"key":"46_CR4","unstructured":"Dupont, E., Doucet, A., Teh, Y.W.: Augmented neural odes (2019)"},{"issue":"5","key":"46_CR5","doi-asserted-by":"publisher","first-page":"R212","DOI":"10.1186\/cc13026","volume":"17","author":"A Garland","year":"2013","unstructured":"Garland, A., Olafson, K., Ramsey, C.D., Yogendran, M., Fransoo, R.: Epidemiology of critically ill patients in intensive care units: a population-based observational study. Crit. Care (London, England) 17(5), R212 (2013)","journal-title":"Crit. Care (London, England)"},{"key":"46_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"A Johnson","year":"2016","unstructured":"Johnson, A., et al.: MIMIC-III, a freely accessible critical care database. Sci. Data 3, 1\u20139 (2016)","journal-title":"Sci. Data"},{"key":"46_CR7","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. Comput. Sci. (2014)"},{"key":"46_CR8","unstructured":"Liu, J., Zhang, Z., Razavian, N.: Deep ehr: chronic disease prediction using medical notes (2018)"},{"key":"46_CR9","doi-asserted-by":"crossref","unstructured":"Ma, F., You, Q., Xiao, H., Chitta, R., Jing, G.: Kame: knowledge-based attention model for diagnosis prediction in healthcare. In: the 27th ACM International Conference (2018)","DOI":"10.1145\/3269206.3271701"},{"key":"46_CR10","doi-asserted-by":"crossref","unstructured":"Peng, X., Long, G., Pan, S., Jiang, J., Niu, Z.: Attentive dual embedding for understanding medical concepts in electronic health records. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2019)","DOI":"10.1109\/IJCNN.2019.8852429"},{"key":"46_CR11","doi-asserted-by":"crossref","unstructured":"Peng, X., Long, G., Shen, T., Wang, S., Jiang, J.: Self-attention enhanced patient journey understanding in healthcare system. arXiv preprint arXiv:2006.10516 (2020)","DOI":"10.1007\/978-3-030-67664-3_43"},{"key":"46_CR12","doi-asserted-by":"crossref","unstructured":"Peng, X., Long, G., Shen, T., Wang, S., Jiang, J., Blumenstein, M.: Temporal self-attention network for medical concept embedding. In: 2019 IEEE International Conference on Data Mining (ICDM), pp. 498\u2013507. IEEE (2019)","DOI":"10.1109\/ICDM.2019.00060"},{"issue":"1","key":"46_CR13","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1038\/s41746-018-0029-1","volume":"1","author":"A Rajkomar","year":"2018","unstructured":"Rajkomar, A., et al.: Scalable and accurate deep learning for electronic health records. NPJ Digit. Med. 1(1), 18 (2018)","journal-title":"NPJ Digit. Med."},{"issue":"3","key":"46_CR14","doi-asserted-by":"publisher","first-page":"424","DOI":"10.7326\/0003-4819-88-3-424","volume":"88","author":"VN Slee","year":"1978","unstructured":"Slee, V.N.: The international classification of diseases: ninth revision (ICD-9). Ann. Int. Med. 88(3), 424\u2013426 (1978)","journal-title":"Ann. Int. Med."},{"key":"46_CR15","unstructured":"Stearns, M.Q., Price, C., Spackman, K.A., Wang, A.Y.: Snomed clinical terms: overview of the development process and project status. In: Proceedings\/AMIA ... Annual Symposium. AMIA Symposium, p. 662 (2001)"},{"key":"46_CR16","unstructured":"Weiss, G.M., Mccarthy, K., Zabar, B.: Cost-sensitive learning vs. sampling: which is best for handling unbalanced classes with unequal error costs? In: International Conference on Data Mining (2007)"},{"issue":"10","key":"46_CR17","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1093\/jamia\/ocy068","volume":"25","author":"C Xiao","year":"2018","unstructured":"Xiao, C., Edward, C., Sun, J.: Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. J. Am. Med. Inform. Assoc. 25(10), 10 (2018)","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"APR","key":"46_CR18","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.artmed.2018.08.004","volume":"95","author":"Y Xue","year":"2019","unstructured":"Xue, Y., Klabjan, D., Luo, Y.: Predicting ICU readmission using grouped physiological and medication trends. Artif. Intell. Med. 95(APR), 27\u201337 (2019)","journal-title":"Artif. Intell. Med."}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-82153-1_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T23:52:30Z","timestamp":1628293950000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-82153-1_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030821524","9783030821531"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-82153-1_46","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tokyo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 August 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ksem2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.cloud-conf.net\/ksem21\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"492","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"164","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"10","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}