{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T11:16:42Z","timestamp":1730200602442,"version":"3.28.0"},"reference-count":34,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T00:00:00Z","timestamp":1671235200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T00:00:00Z","timestamp":1671235200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,12,17]]},"DOI":"10.1109\/bigdata55660.2022.10020963","type":"proceedings-article","created":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T14:35:23Z","timestamp":1674743723000},"page":"2082-2092","source":"Crossref","is-referenced-by-count":1,"title":["Sub-Sequence Graph Representation Learning on High Variability Data for Dynamic Risk Prediction in Critical Care"],"prefix":"10.1109","author":[{"given":"Ankur","family":"Teredesai","sequence":"first","affiliation":[{"name":"University of Washington and CueZen Inc,Engineering and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sijin","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Washington,Engineering and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tucker","family":"Stewart","sequence":"additional","affiliation":[{"name":"University of Washington,Engineering and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juhua","family":"Hu","sequence":"additional","affiliation":[{"name":"University of Washington,Engineering and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Armaan","family":"Thakker","sequence":"additional","affiliation":[{"name":"UW and The Harker School,Engineering and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katherine","family":"Stern","sequence":"additional","affiliation":[{"name":"University of Washington,Department of Surgery"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Grant E","family":"O'Keefe","sequence":"additional","affiliation":[{"name":"University of Washington,Department of Surgery"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0211057"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2019.103395"},{"key":"ref34","article-title":"Exact solutions to the nonlinear dynamics of learning in deep linear neural networks","author":"saxe","year":"2013","journal-title":"arXiv preprint arXiv 1312 6120"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2019.04.027"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2019.103395"},{"key":"ref31","first-page":"103651","article-title":"Evaluating machine learning models for sepsis prediction: A systematic review of methodologies","author":"deng","year":"2021","journal-title":"ISCIE"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2022.104689"},{"key":"ref11","first-page":"185","article-title":"Prediction of sepsis and in-hospital mortality using electronic health records","volume":"57","author":"khojandi","year":"0","journal-title":"Methods Inf Med"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219950"},{"key":"ref10","article-title":"Data-driven discovery of a novel sepsis pre-shock state predicts impending septic shock in the icu","volume":"9","author":"liu","year":"2019","journal-title":"Scientific Reports"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132926"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4939-1776-1"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1703058"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1088\/0967-3334\/31\/6\/004"},{"key":"ref16","article-title":"The consequences of the framing of machine learning risk prediction models: Evaluation of sepsis in general wards","author":"lauritsen","year":"2021","journal-title":"arXiv preprint arXiv 2101 10955"},{"key":"ref19","article-title":"Temporal convolutional networks and dynamic time warping can drastically improve the early prediction of sepsis","author":"moor","year":"2019","journal-title":"arXiv preprint arXiv 1902 03187"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2894570"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358010"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301346"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401319"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/547"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403284"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2020.01.021"},{"key":"ref28","article-title":"Hospital acquired infections","author":"monegro","year":"2020","journal-title":"StatPearls"},{"key":"ref27","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"chung","year":"2014","journal-title":"arXiv preprint arXiv 1412 3555"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0000000000003521"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjsurg.2019.03.005"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s00134-017-4683-6"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1002\/bjs.11361"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(20)31609-3"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1093\/ofid\/ofy313"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjms.2018.02.007"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1097\/01.CCM.0000217961.75225.E9"}],"event":{"name":"2022 IEEE International Conference on Big Data (Big Data)","start":{"date-parts":[[2022,12,17]]},"location":"Osaka, Japan","end":{"date-parts":[[2022,12,20]]}},"container-title":["2022 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10020192\/10020156\/10020963.pdf?arnumber=10020963","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T17:09:42Z","timestamp":1676912982000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10020963\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,17]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/bigdata55660.2022.10020963","relation":{},"subject":[],"published":{"date-parts":[[2022,12,17]]}}}