{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T16:09:26Z","timestamp":1772726966134,"version":"3.50.1"},"publisher-location":"Republic and Canton of Geneva, Switzerland","reference-count":36,"publisher":"International World Wide Web Conferences Steering Committee","license":[{"start":{"date-parts":[[2017,4,3]],"date-time":"2017-04-03T00:00:00Z","timestamp":1491177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61403390 U1435221"],"award-info":[{"award-number":["61403390 U1435221"]}]},{"name":"National Key Research and Development Program","award":["2016YFB1001000"],"award-info":[{"award-number":["2016YFB1001000"]}]},{"name":"CCF-Venustech Hongyan Research Fund"},{"name":"CCF-Tencent Open Fund"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2017,4,3]]},"DOI":"10.1145\/3038912.3052604","type":"proceedings-article","created":{"date-parts":[[2017,4,6]],"date-time":"2017-04-06T13:30:38Z","timestamp":1491485438000},"page":"685-693","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":47,"title":["Blood Pressure Prediction via Recurrent Models with Contextual Layer"],"prefix":"10.1145","author":[{"given":"Xiaohan","family":"Li","sequence":"first","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Shu","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Liang","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2017,4,3]]},"reference":[{"issue":"2","key":"e_1_3_2_1_1_1","first-page":"212","article-title":"Joint language and translation modeling with recurrent neural networks","volume":"74","author":"Auli M.","year":"2013","unstructured":"M. Auli, M. Galley, C. Quirk, and G. Zweig. Joint language and translation modeling with recurrent neural networks. American Journal of Psychoanalysis, 74(2):212--3, 2013.","journal-title":"American Journal of Psychoanalysis"},{"key":"e_1_3_2_1_2_1","volume-title":"An analysis of four missing data treatment methods for supervised learning. Applied Artificial Intelligence, 17(5--6):519--533","author":"Batista G. E.","year":"2003","unstructured":"G. E. Batista and M. C. Monard. An analysis of four missing data treatment methods for supervised learning. Applied Artificial Intelligence, 17(5--6):519--533, 2003."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.38121.684410.AE"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/49.611161"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.289.19.2560"},{"key":"e_1_3_2_1_6_1","volume-title":"Eprint Arxiv","author":"Chung J.","year":"2014","unstructured":"J. Chung, C. Gulcehre, K. H. Cho, and Y. Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. Eprint Arxiv, 2014."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-23344-4_25"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature10405"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1159\/000047643"},{"key":"e_1_3_2_1_10_1","first-page":"1189","volume-title":"a gradient boosting machine. Annals of statistics","author":"Friedman J. H.","year":"2001","unstructured":"J. H. Friedman. Greedy function approximation: a gradient boosting machine. Annals of statistics, pages 1189--1232, 2001."},{"key":"e_1_3_2_1_11_1","first-page":"49","volume-title":"NIPS","author":"Gerrard M.","year":"2008","unstructured":"M. Gerrard, F. X. Gibbons, A. E. Houlihan, M. L. Stock, and E. A. Pomery. Probabilistic detection of short events, with application to critical care monitoring. In NIPS, pages 49--56, 2008."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300015015"},{"issue":"5","key":"e_1_3_2_1_13_1","first-page":"637635","article-title":"Predicting increased blood pressure using machine learning","volume":"2014","author":"Golino H. F.","year":"2014","unstructured":"H. F. Golino, L. S. Amaral, S. F. Duarte, C. M. Gomes, T. J. Soares, L. A. Dos Reis, and J. Santos. Predicting increased blood pressure using machine learning. Journal of Obesity, 2014(5):637635--637635, 2014.","journal-title":"Journal of Obesity"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.137"},{"key":"e_1_3_2_1_15_1","volume-title":"Speech recognition with deep recurrent neural networks. 1(2003):6645--6649","author":"Graves A.","year":"2013","unstructured":"A. Graves, A. R. Mohamed, and G. Hinton. Speech recognition with deep recurrent neural networks. 1(2003):6645--6649, 2013."},{"key":"e_1_3_2_1_16_1","volume-title":"Pd disease state assessment in naturalistic environments using deep learning","author":"Hammerla N.","year":"2015","unstructured":"N. Hammerla, J. Fisher, P. Andras, L. Rochester, R. Walker, and T. Ploetz. Pd disease state assessment in naturalistic environments using deep learning. 2015."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2013.284427"},{"key":"e_1_3_2_1_19_1","volume-title":"Gradient Flow in Recurrent Nets: The Difficulty of Learning LongTerm Dependencies","author":"Kolen J.","year":"2009","unstructured":"J. Kolen and S. Kremer. Gradient Flow in Recurrent Nets: The Difficulty of Learning LongTerm Dependencies. Wiley-IEEE Press, 2009."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","first-page":"2877","DOI":"10.21437\/Interspeech.2011-720","volume-title":"INTERSPEECH","volume":"11","author":"Kombrink S.","year":"2011","unstructured":"S. Kombrink, T. Mikolov, M. Karafi\u00e4t, and L. Burget. Recurrent neural network based language modeling in meeting recognition. In INTERSPEECH, volume 11, pages 2877--2880, 2011."},{"key":"e_1_3_2_1_21_1","volume-title":"Computer Science","author":"Lipton Z. C.","year":"2015","unstructured":"Z. C. Lipton, D. C. Kale, C. Elkan, and R. Wetzell. Learning to diagnose with lstm recurrent neural networks. Computer Science, 2015."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0135"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.5555\/3015812.3015841"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1097\/HJH.0b013e3282f2fdd4"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/eht151"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2010-343"},{"key":"e_1_3_2_1_27_1","volume-title":"http:\/\/www.who.int\/features\/qa\/82\/en\/","author":"W. H. Organization","year":"2015","unstructured":"W. H. Organization. Q&As on hypertension. http:\/\/www.who.int\/features\/qa\/82\/en\/, 2015."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.18626"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.127"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/SMARTCOMP.2016.7501681"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1023\/B:STCO.0000035301.49549.88"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2788588"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrneph.2011.108"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1161\/01.CIR.97.18.1837"},{"key":"e_1_3_2_1_35_1","first-page":"1","volume-title":"Predicting systolic blood pressure using machine learning","author":"Wu T. H.","year":"2015","unstructured":"T. H. Wu, K. H. Pang, and W. Y. Kwong. Predicting systolic blood pressure using machine learning. pages 1--6, 2015."},{"key":"e_1_3_2_1_36_1","volume-title":"Computer Science","author":"Zhang Y.","year":"2014","unstructured":"Y. Zhang, H. Dai, C. Xu, J. Feng, T. Wang, J. Bian, B. Wang, and T. Y. Liu. Sequential click prediction for sponsored search with recurrent neural networks. Computer Science, 2014"}],"event":{"name":"WWW '17: 26th International World Wide Web Conference","location":"Perth Australia","acronym":"WWW '17","sponsor":["IW3C2 International World Wide Web Conference Committee","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the 26th International Conference on World Wide Web"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3038912.3052604","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3038912.3052604","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:24:06Z","timestamp":1750220646000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3038912.3052604"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,4,3]]},"references-count":36,"alternative-id":["10.1145\/3038912.3052604","10.5555\/3038912"],"URL":"https:\/\/doi.org\/10.1145\/3038912.3052604","relation":{},"subject":[],"published":{"date-parts":[[2017,4,3]]},"assertion":[{"value":"2017-04-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}