{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:26:30Z","timestamp":1742995590988,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030772109"},{"type":"electronic","value":"9783030772116"}],"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\/978-3-030-77211-6_26","type":"book-chapter","created":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T23:06:27Z","timestamp":1623107187000},"page":"240-245","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Disentangled Hyperspherical Clustering for Sepsis Phenotyping"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1876-0855","authenticated-orcid":false,"given":"Cheng","family":"Cheng","sequence":"first","affiliation":[]},{"given":"Jason","family":"Kennedy","sequence":"additional","affiliation":[]},{"given":"Christopher","family":"Seymour","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1693-9082","authenticated-orcid":false,"given":"Jeremy C.","family":"Weiss","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,8]]},"reference":[{"key":"26_CR1","unstructured":"Eastwood, C., Williams, C.K.I.: A framework for the quantitative evaluation of disentangled representations. In: ICLR (2018)"},{"key":"26_CR2","unstructured":"Fillmore, N., et al.: Hypersphere clustering to characterize healthcare providers using prescriptions and procedures from medicare claims data. In: AMIA (2019)"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Guo, X., et al.: Improved deep embedded clustering with local structure preservation. In: IJCAI-17, pp. 1753\u20131759 (2017)","DOI":"10.24963\/ijcai.2017\/243"},{"issue":"5","key":"26_CR4","doi-asserted-by":"publisher","first-page":"814","DOI":"10.1007\/s00134-015-3764-7","volume":"41","author":"DB Knox","year":"2015","unstructured":"Knox, D.B., et al.: Phenotypic clusters within sepsis-associated multiple organ dysfunction syndrome. Intensive Care Med. 41(5), 814\u2013822 (2015)","journal-title":"Intensive Care Med."},{"key":"26_CR5","unstructured":"Locatello, F., et al.: Challenging common assumptions in the unsupervised learning of disentangled representations. In: Proceedings of the 36th ICML (2019)"},{"issue":"4","key":"26_CR6","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.molmed.2014.01.007","volume":"20","author":"JC Marshall","year":"2014","unstructured":"Marshall, J.C.: Why have clinical trials in sepsis failed? Trends Mol. Med. 20(4), 195\u2013203 (2014)","journal-title":"Trends Mol. Med."},{"issue":"14","key":"26_CR7","doi-asserted-by":"publisher","first-page":"1416","DOI":"10.1001\/jama.2019.12587","volume":"322","author":"J Moser","year":"2019","unstructured":"Moser, J., et al.: Identifying sepsis phenotypes. JAMA 322(14), 1416\u20131416 (2019)","journal-title":"JAMA"},{"key":"26_CR8","unstructured":"Ridgeway, K., Mozer, M.C.: Learning deep disentangled embeddings with the f-statistic loss. In: Advances in NIPS 31, pp. 185\u2013194. Curran Associates, Inc. (2018)"},{"issue":"20","key":"26_CR9","doi-asserted-by":"publisher","first-page":"2003","DOI":"10.1001\/jama.2019.5791","volume":"321","author":"CW Seymour","year":"2019","unstructured":"Seymour, C.W., et al.: Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. JAMA 321(20), 2003\u20132017 (2019)","journal-title":"JAMA"},{"issue":"8","key":"26_CR10","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1001\/jama.2016.0289","volume":"315","author":"M Shankar-Hari","year":"2016","unstructured":"Shankar-Hari, M., et al.: Developing a new definition and assessing new clinical criteria for septic shock. JAMA 315(8), 775\u2013787 (2016)","journal-title":"JAMA"},{"key":"26_CR11","doi-asserted-by":"publisher","first-page":"1502","DOI":"10.3389\/fimmu.2018.01502","volume":"9","author":"VB Talisa","year":"2018","unstructured":"Talisa, V.B., et al.: Arguing for adaptive clinical trials in sepsis. Front. Immunol. 9, 1502 (2018)","journal-title":"Front. Immunol."},{"key":"26_CR12","unstructured":"Xie, J., et al.: Unsupervised deep embedding for clustering analysis. In: ICML (2016)"},{"key":"26_CR13","unstructured":"Yang, B., et al.: Towards k-means-friendly spaces: simultaneous deep learning and clustering. CoRR abs\/1610.04794 (2016)"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-77211-6_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T10:06:05Z","timestamp":1706695565000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-77211-6_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030772109","9783030772116"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-77211-6_26","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":"8 June 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIME","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Medicine","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aime2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/aime21.aimedicine.info\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}