{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T18:28:03Z","timestamp":1729621683533,"version":"3.28.0"},"reference-count":47,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"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":[[2019,12]]},"DOI":"10.1109\/bigdata47090.2019.9005602","type":"proceedings-article","created":{"date-parts":[[2020,2,25]],"date-time":"2020-02-25T06:05:34Z","timestamp":1582610734000},"page":"145-154","source":"Crossref","is-referenced-by-count":1,"title":["CTC-Attention based Non-Parametric Inference Modeling for Clinical State Progression"],"prefix":"10.1109","author":[{"given":"Riazat","family":"Ryan","sequence":"first","affiliation":[]},{"given":"Handong","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Ming","family":"Shao","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","article-title":"Deep reinforcement learning for sepsis treatment","author":"raghu","year":"2017","journal-title":"arXiv preprint arXiv 1711 07128"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ISCSLP.2018.8706675"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2017.7953075"},{"key":"ref31","article-title":"A‘ novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks","author":"liwicki","year":"2007","journal-title":"International Conference on Document Analysis and Recognition"},{"key":"ref30","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v32i1.11635","article-title":"Attend and diagnose: Clinical time series analysis using attention models","author":"song","year":"2018","journal-title":"AAAI"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-5307"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/322"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.2196\/medinform.8960"},{"key":"ref34","first-page":"1","article-title":"Temporal pattern attention for multivariate time series forecasting","author":"shih","year":"2018","journal-title":"Machine Learning"},{"key":"ref10","first-page":"5998","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"NeurIPS"},{"key":"ref40","first-page":"809","article-title":"Predictive modeling of the risk of acute kidney injury in critical care: A systematic investigation of the class imbalance problem","volume":"2019","author":"xu","year":"2019","journal-title":"AMIA Summits on Translational Science Proceedings"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143891"},{"key":"ref12","volume":"3","author":"pandit","year":"1983","journal-title":"Time Series and System Analysis with Applications"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.fss.2007.11.003"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"123","DOI":"10.4103\/jfcm.JFCM_51_18","article-title":"Forecasting indian infant mortality rate: An application of autoregressive integrated moving average model","volume":"26","author":"mishra","year":"2019","journal-title":"Journal of Family & Community Medicine"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2008.02.022"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(01)00702-0"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref18","article-title":"Deep learning for time-series analysis","author":"gamboa","year":"2017","journal-title":"arXiv preprint arXiv 1701 01887"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-018-0029-1"},{"key":"ref28","first-page":"3915","article-title":"Sgm: Sequence generation model for multi-label classification","author":"yang","year":"2018","journal-title":"International Conference on Computational Linguistics"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"26094","DOI":"10.1038\/srep26094","article-title":"Deep patient: an unsupervised representation to predict the future of patients from the electronic health records","volume":"6","author":"miotto","year":"2016","journal-title":"Scientific Reports"},{"key":"ref27","first-page":"6830","article-title":"Style transfer from nonparallel text by cross-alignment","author":"shen","year":"2017","journal-title":"NeurIPS"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2017.2767063"},{"key":"ref6","first-page":"82","article-title":"Predicting intervention onset in the icu with switching state space models","volume":"2017","author":"ghassemi","year":"2017","journal-title":"AMIA Summits on Translational Science Proceedings"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098126"},{"key":"ref5","first-page":"30","article-title":"Deepcare: A deep dynamic memory model for predictive medicine","author":"pham","year":"2016","journal-title":"Pacific-Asia Conference on Knowledge Discovery and Data Mining"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/2716262"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1378\/chest.112.1.235"},{"key":"ref2","article-title":"Hospitals use of electronic health records data, 2015-2017","author":"parasrampuria","year":"2019","journal-title":"ONC Data Brief"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/78.650093"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.15265\/IY-2014-0003"},{"key":"ref46","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"NeurIPS"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICHI.2016.16"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/767"},{"key":"ref22","article-title":"Learning to diagnose with lstm recurrent neural networks","author":"lipton","year":"2015","journal-title":"arXiv preprint arXiv 1511 05271"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330957"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"6085","DOI":"10.1038\/s41598-018-24271-9","article-title":"Recurrent neural networks for multivariate time series with missing values","volume":"8","author":"che","year":"2018","journal-title":"Scientific Reports"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.pneurobio.2011.09.005"},{"key":"ref24","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","author":"sutskever","year":"2014","journal-title":"NeurIPS"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.jelectrocard.2017.08.013"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783365"},{"key":"ref44","first-page":"2921","article-title":"Multi-view clustering via deep matrix factorization","author":"zhao","year":"2017","journal-title":"AAAI"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1166"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1002\/mds.27361"},{"key":"ref25","article-title":"Neural machine translation by jointly learning to align and translate","author":"bahdanau","year":"2014","journal-title":"arXiv preprint arXiv 1409 0473"}],"event":{"name":"2019 IEEE International Conference on Big Data (Big Data)","start":{"date-parts":[[2019,12,9]]},"location":"Los Angeles, CA, USA","end":{"date-parts":[[2019,12,12]]}},"container-title":["2019 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8986695\/9005444\/09005602.pdf?arnumber=9005602","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T15:12:08Z","timestamp":1695827528000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9005602\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":47,"URL":"https:\/\/doi.org\/10.1109\/bigdata47090.2019.9005602","relation":{},"subject":[],"published":{"date-parts":[[2019,12]]}}}