{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T10:17:24Z","timestamp":1778408244976,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":52,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T00:00:00Z","timestamp":1617840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,4,8]]},"DOI":"10.1145\/3450439.3451872","type":"proceedings-article","created":{"date-parts":[[2021,3,23]],"date-time":"2021-03-23T22:25:27Z","timestamp":1616538327000},"page":"236-245","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["T-DPSOM"],"prefix":"10.1145","author":[{"given":"Laura","family":"Manduchi","sequence":"first","affiliation":[{"name":"ETH Zurich, Z\u00fcrich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthias","family":"H\u00fcser","sequence":"additional","affiliation":[{"name":"ETH Z\u00fcrich, Z\u00fcrich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Faltys","sequence":"additional","affiliation":[{"name":"ETH Z\u00fcrich, Z\u00fcrich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julia","family":"Vogt","sequence":"additional","affiliation":[{"name":"ETH Z\u00fcrich, Z\u00fcrich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gunnar","family":"R\u00e4tsch","sequence":"additional","affiliation":[{"name":"ETH Z\u00fcrich, Z\u00fcrich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vincent","family":"Fortuin","sequence":"additional","affiliation":[{"name":"ETH Z\u00fcrich, Z\u00fcrich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,4,8]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Clustering with Deep Learning: Taxonomy and New Methods. CoRR abs\/1801.07648","author":"Aljalbout Elie","year":"2018"},{"key":"e_1_3_2_1_2_1","first-page":"7","article-title":"Identification of Acute Kidney Injury Subphenotypes with Differing Molecular Signatures and Responses to Vasopressin","volume":"199","author":"Bhatraju Pavan K","year":"2019","journal-title":"Therapy. Am. J. Respir. Crit. Care Med."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976698300017953"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1136\/thoraxjnl-2016-209719"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1186\/cc8868"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2213-2600(14)70097-9"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(93)90011-K"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2009.2013708"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/645803.669507"},{"key":"e_1_3_2_1_10_1","volume-title":"Deep Embedded SOM: Joint Representation Learning and Self-Organization. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN","author":"Forest Florent","year":"2019"},{"key":"e_1_3_2_1_11_1","volume-title":"SOM-VAE: Interpretable Discrete Representation Learning on Time Series. arXiv preprint arXiv:1806.02199","author":"Fortuin Vincent","year":"2018"},{"key":"e_1_3_2_1_12_1","volume-title":"Multivariate Time Series Imputation with Variational Autoencoders. arXiv preprint arXiv:1907.04155","author":"Fortuin Vincent","year":"2019"},{"key":"e_1_3_2_1_13_1","volume-title":"Meta-Learning Mean Functions for Gaussian Processes. arXiv preprint arXiv:1901.08098","author":"Fortuin Vincent","year":"2019"},{"key":"e_1_3_2_1_14_1","volume-title":"Unsupervised Scalable Representation Learning for Multivariate Time Series. arXiv preprint arXiv:1901.10738","author":"Franceschi Jean-Yves","year":"2019"},{"key":"e_1_3_2_1_15_1","volume-title":"Six subphenotypes in septic shock: Latent class analysis of the PROWESS Shock study. J. Crit. Care 47 (Oct","author":"G\u00e5rdlund Bengt","year":"2018"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"S. Van Gassen B. Callebaut Mary J. van Helden B. Lambrecht P. Demeester T. Dhaene and Y. Saeys. 2015. FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data. Cytometry Part A 87 (2015).  S. Van Gassen B. Callebaut Mary J. van Helden B. Lambrecht P. Demeester T. Dhaene and Y. Saeys. 2015. FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data. Cytometry Part A 87 (2015).","DOI":"10.1002\/cyto.a.22625"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.545"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.5555\/3172077.3172131"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"C. Higuera K. Gardiner and K. Cios. 2015. Self-Organizing Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down Syndrome. PLoS ONE 10 (2015).  C. Higuera K. Gardiner and K. Cios. 2015. Self-Organizing Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down Syndrome. PLoS ONE 10 (2015).","DOI":"10.1371\/journal.pone.0129126"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1214\/009053607000000677"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/IEMBS.2010.5625995"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-04898-2_455"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00134-015-3764-7"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.58325"},{"key":"e_1_3_2_1_26_1","unstructured":"Teuvo Kohonen. 1995. The Adaptive-Subspace SOM (ASSOM) and its use for the Implementation of Invariant Feature Detection.  Teuvo Kohonen. 1995. The Adaptive-Subspace SOM (ASSOM) and its use for the Implementation of Invariant Feature Detection."},{"key":"e_1_3_2_1_27_1","volume-title":"Vignesh Ram Somnath, and Manfred Claassen","author":"Kopf Andreas","year":"2019"},{"key":"e_1_3_2_1_28_1","unstructured":"Rahul G Krishnan Uri Shalit and David Sontag. 2016. Deep Kalman Filters. (2016).  Rahul G Krishnan Uri Shalit and David Sontag. 2016. Deep Kalman Filters. (2016)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2015.7280357"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2060208"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"T. A. McQueen A. A. Hopgood J. A. Tepper and T. J. Allen. 2004. A Recurrent Self-Organizing Map for Temporal Sequence Processing. In Applications and Science in Soft Computing Ahamad Lotfi and Jonathan M. Garibaldi (Eds.). Springer Berlin Heidelberg Berlin Heidelberg 3--8.  T. A. McQueen A. A. Hopgood J. A. Tepper and T. J. Allen. 2004. A Recurrent Self-Organizing Map for Temporal Sequence Processing. In Applications and Science in Soft Computing Ahamad Lotfi and Jonathan M. Garibaldi (Eds.). Springer Berlin Heidelberg Berlin Heidelberg 3--8.","DOI":"10.1007\/978-3-540-45240-9_1"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jns.2015.10.032"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/37.1-2.17"},{"key":"e_1_3_2_1_35_1","volume-title":"Jesse D Raffa, Leo A Celi, Roger G Mark, and Omar Badawi.","author":"Pollard Tom J","year":"2018"},{"key":"e_1_3_2_1_36_1","volume-title":"Generating Diverse High-Fidelity Images with VQ-VAE-2. arXiv preprint arXiv:1906.00446","author":"Razavi Ali","year":"2019"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2213-2600(20)30124-7"},{"key":"e_1_3_2_1_38_1","volume-title":"MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis. arXiv preprint arXiv:1909.12637","author":"Rosnati Margherita","year":"2019"},{"key":"e_1_3_2_1_39_1","volume-title":"Proceedings of the National Academy of Sciences of the United States of America 96 6","author":"Tamayo P.","year":"1999"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-011-9752-8"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CIDM.2014.7008682"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2008.10.006"},{"key":"e_1_3_2_1_43_1","first-page":"7","article-title":"Situation Awareness-Oriented Patient Monitoring with Visual Patient Technology","volume":"20","author":"Tscholl David Werner","year":"2020","journal-title":"A Qualitative Review of the Primary Research. Sensors"},{"key":"e_1_3_2_1_44_1","volume-title":"Neural Discrete Representation Learning. CoRR abs\/1711.00937","author":"van den Oord A\u00e4ron","year":"2017"},{"key":"e_1_3_2_1_45_1","volume-title":"Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research","volume":"391","author":"van der Maaten Laurens","year":"2009"},{"key":"e_1_3_2_1_46_1","volume-title":"The loracs prior for vaes: Letting the trees speak for the data. arXiv preprint arXiv:1810.06891","author":"Vikram Sharad","year":"2018"},{"key":"e_1_3_2_1_47_1","first-page":"3","article-title":"The coming era of precision medicine for intensive care","volume":"21","author":"Vincent Jean-Louis","year":"2017","journal-title":"Crit. Care"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(02)00072-2"},{"key":"e_1_3_2_1_49_1","first-page":"1","article-title":"ARDS Subphenotypes: Understanding a Heterogeneous","volume":"24","author":"Wilson Jennifer G","year":"2020","journal-title":"Syndrome. Crit. Care"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/0169-7439(87)80084-9"},{"key":"e_1_3_2_1_51_1","volume-title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. CoRR abs\/1708.07747","author":"Xiao Han","year":"2017"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045442"}],"event":{"name":"ACM CHIL '21: ACM Conference on Health, Inference, and Learning","location":"Virtual Event USA","acronym":"ACM CHIL '21","sponsor":["ACM Association for Computing Machinery"]},"container-title":["Proceedings of the Conference on Health, Inference, and Learning"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3450439.3451872","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3450439.3451872","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:45Z","timestamp":1750191525000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3450439.3451872"}},"subtitle":["an interpretable clustering method for unsupervised learning of patient health states"],"short-title":[],"issued":{"date-parts":[[2021,4,8]]},"references-count":52,"alternative-id":["10.1145\/3450439.3451872","10.1145\/3450439"],"URL":"https:\/\/doi.org\/10.1145\/3450439.3451872","relation":{},"subject":[],"published":{"date-parts":[[2021,4,8]]},"assertion":[{"value":"2021-04-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}