{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:07:56Z","timestamp":1750306076306,"version":"3.41.0"},"publisher-location":"New York, New York, USA","reference-count":23,"publisher":"ACM Press","license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1145\/3105831.3105858","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T19:35:01Z","timestamp":1501270501000},"page":"14-19","source":"Crossref","is-referenced-by-count":0,"title":["A New Temporal Abstraction for Health Diagnosis Prediction using Deep Recurrent Networks"],"prefix":"10.1145","author":[{"given":"Alireza","family":"Manashty","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of New Brunswick, Saint John, New Brunswick, Canada"}]},{"given":"Janet Light","family":"Thomson","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Saint John, New Brunswick, Canada"}]}],"member":"320","reference":[{"key":"key-10.1145\/3105831.3105858-1","doi-asserted-by":"crossref","unstructured":"X.W. Gao, R. Hui, Z. Tian, Classification of CT brain images based on deep learning networks, Comput. Methods Programs Biomed. 138 (2017) 49--56. doi:10.1016\/j.cmpb.2016.10.007.","DOI":"10.1016\/j.cmpb.2016.10.007"},{"key":"key-10.1145\/3105831.3105858-2","doi-asserted-by":"crossref","unstructured":"S. Pang, Z. Yu, M.A. Orgun, A Novel End-to-End Classifier Using Domain Transferred Deep Convolutional Neural Networks for Biomedical Images, Comput. Methods Programs Biomed. 140 (2017) 283--293. doi:10.1016\/j.cmpb.2016.12.019.","DOI":"10.1016\/j.cmpb.2016.12.019"},{"key":"key-10.1145\/3105831.3105858-3","doi-asserted-by":"crossref","unstructured":"R. Miotto, L. Li, B.A. Kidd, J.T. Dudley, Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records, Sci. Rep. 6 (2016) 26094. http:\/\/dx.doi.org\/10.1038\/srep26094.","DOI":"10.1038\/srep26094"},{"key":"key-10.1145\/3105831.3105858-4","doi-asserted-by":"crossref","unstructured":"A.I. Aviles, S.M. Alsaleh, E. Montseny, P. Sobrevilla, A. Casals, A Deep-Neuro-Fuzzy Approach for Estimating the Interaction Forces in Robotic Surgery, (2016) 1113--1119.","DOI":"10.1109\/FUZZ-IEEE.2016.7737812"},{"key":"key-10.1145\/3105831.3105858-5","doi-asserted-by":"crossref","unstructured":"A. Manashty, J. Light, U. Yadav, Healthcare event aggregation lab (HEAL), a knowledge sharing platform for anomaly detection and prediction, in: 2015 17th Int. Conf. E-Health Networking, Appl. Serv. Heal. 2015, IEEE, Boston, MA, 2016: pp. 648--652. doi:10.1109\/HealthCom.2015.7454584.","DOI":"10.1109\/HealthCom.2015.7454584"},{"key":"key-10.1145\/3105831.3105858-6","doi-asserted-by":"crossref","unstructured":"A.R.M. Forkan, I. Khalil, Z. Tari, S. Foufou, A. Bouras, A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living, Pattern Recognit. 48 (2014) 628--641. doi:10.1016\/j.patcog.2014.07.007.","DOI":"10.1016\/j.patcog.2014.07.007"},{"key":"key-10.1145\/3105831.3105858-7","doi-asserted-by":"crossref","unstructured":"D. Elbert, H. Storf, M. Eisenbarth, Ö. Ünalan, M. Schmitt, An approach for detecting deviations in daily routine for long-term behavior analysis, in: Proc. 5th Int. ICST Conf. Pervasive Comput. Technol. Healthc., IEEE, 2011: pp. 426--433. doi:10.4108\/icst.pervasivehealth.2011.246089.","DOI":"10.4108\/icst.pervasivehealth.2011.246089"},{"key":"key-10.1145\/3105831.3105858-8","doi-asserted-by":"crossref","unstructured":"R. Miotto, L. Li, B.A. Kidd, J.T. Dudley, Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records, Sci. Rep. 6 (2016) 26094. http:\/\/dx.doi.org\/10.1038\/srep26094.","DOI":"10.1038\/srep26094"},{"key":"key-10.1145\/3105831.3105858-9","doi-asserted-by":"crossref","unstructured":"A. Manashty, J. Light, Cloud Platforms for IoE Healthcare Context Awareness and Knowledge Sharing, in: Beyond the Internet of Things: Everything Interconnected, Springer-Verlag, 2016.","DOI":"10.1007\/978-3-319-50758-3_12"},{"key":"key-10.1145\/3105831.3105858-10","doi-asserted-by":"crossref","unstructured":"A. Forkan, I. Khalil, Z. Tari, CoCaMAAL: A cloud-oriented context-aware middleware in ambient assisted living, Futur. Gener. Comput. Syst. 35 (2014) 114--127. doi:10.1016\/j.future.2013.07.009.","DOI":"10.1016\/j.future.2013.07.009"},{"key":"key-10.1145\/3105831.3105858-11","doi-asserted-by":"crossref","unstructured":"C. Doukas, I. Maglogiannis, Bringing IoT and cloud computing towards pervasive healthcare, in: Proc. - 6th Int. Conf. Innov. Mob. Internet Serv. Ubiquitous Comput. IMIS 2012, 2012: pp. 922--926. doi:10.1109\/IMIS.2012.26.","DOI":"10.1109\/IMIS.2012.26"},{"key":"key-10.1145\/3105831.3105858-12","doi-asserted-by":"crossref","unstructured":"J. Cubo, A. Nieto, E. Pimentel, A Cloud-Based Internet of Things Platform for Ambient Assisted Living, 2014. doi:10.3390\/s140814070.","DOI":"10.3390\/s140814070"},{"key":"key-10.1145\/3105831.3105858-13","doi-asserted-by":"crossref","unstructured":"A. Forkan, I. Khalil, A. Ibaida, Z. Tari, BDCaM: Big Data for Context-aware Monitoring - A Personalized Knowledge Discovery Framework for Assisted Healthcare, IEEE Trans. Cloud Comput. PP (2015) 1--1. doi:10.1109\/TCC.2015.2440269.","DOI":"10.1109\/TCC.2015.2440269"},{"key":"key-10.1145\/3105831.3105858-14","doi-asserted-by":"crossref","unstructured":"H.-H. Rau, C.-Y. Hsu, Y.-A. Lin, S. Atique, A. Fuad, L.-M. Wei, M.-H. Hsu, Development of a web-based liver cancer prediction model for type II diabetes patients by using an artificial neural network, Comput. Methods Programs Biomed. 125 (2016) 58--65. doi:10.1016\/j.cmpb.2015.11.009.","DOI":"10.1016\/j.cmpb.2015.11.009"},{"key":"key-10.1145\/3105831.3105858-15","doi-asserted-by":"crossref","unstructured":"Z. Goli-Malekabadi, M. Sargolzaei-Javan, M.K. Akbari, An effective model for store and retrieve big health data in cloud computing, Comput. Methods Programs Biomed. 132 (2016) 75--82. doi:10.1016\/j.cmpb.2016.04.016.","DOI":"10.1016\/j.cmpb.2016.04.016"},{"key":"key-10.1145\/3105831.3105858-16","doi-asserted-by":"crossref","unstructured":"A.E.W. Johnson, T.J. Pollard, L. Shen, L.H. Lehman, M. Feng, M. Ghassemi, B. Moody, P. Szolovits, L. Anthony Celi, R.G. Mark, MIMIC-III, a freely accessible critical care database, Sci. Data. 3 (2016) 160035. http:\/\/dx.doi.org\/10.1038\/sdata.2016.35.","DOI":"10.1038\/sdata.2016.35"},{"key":"key-10.1145\/3105831.3105858-17","doi-asserted-by":"crossref","unstructured":"I. Batal, G.F. Cooper, D. Fradkin, J. Harrison, F. Moerchen, M. Hauskrecht, An efficient pattern mining approach for event detection in multivariate temporal data, Knowl. Inf. Syst. 46 (2016) 115--150. doi:10.1007\/s10115-015-0819-6.","DOI":"10.1007\/s10115-015-0819-6"},{"key":"key-10.1145\/3105831.3105858-18","doi-asserted-by":"crossref","unstructured":"A. Rahim, M. Forkan, I. Khalil, M. Atiquzzaman, ViSiBiD: A learning model for early discovery and real-time prediction of severe clinical events using vital signs as big data, Comput. Networks. 113 (2017) 244--257. doi:10.1016\/j.comnet.2016.12.019.","DOI":"10.1016\/j.comnet.2016.12.019"},{"key":"key-10.1145\/3105831.3105858-19","unstructured":"X. Xi, Further applications of higher-order Markov chains and developments in regime-switching models, 2012. http:\/\/ir.lib.uwo.ca\/etd\/678\/."},{"key":"key-10.1145\/3105831.3105858-20","doi-asserted-by":"crossref","unstructured":"H.T. Siegelmann, E.D. Sontag, Turing computability with neural nets, Appl. Math. Lett. 4 (1991) 77--80. doi:10.1016\/0893-9659(91)90080-F.","DOI":"10.1016\/0893-9659(91)90080-F"},{"key":"key-10.1145\/3105831.3105858-21","unstructured":"K. Greff, J. Koutn, LSTM: A Search Space Odyssey, (1997)."},{"key":"key-10.1145\/3105831.3105858-22","unstructured":"D. Yu, A. Eversole, M. Seltzer, K. Yao, Z. Huang, B. Guenter, O. Kuchaiev, Y. Zhang, F. Seide, H. Wang, J. Droppo, G. Zweig, C. Rossbach, J. Currey, J. Gao, A. May, B. Peng, A. Stolcke, M. Slaney, An Introduction to Computational Networks and the Computational Network Toolkit, 2015."},{"key":"key-10.1145\/3105831.3105858-23","unstructured":"X. Glorot, A. Bordes, Y. Bengio, Deep sparse rectifier neural networks, AISTATS '11 Proc. 14th Int. Conf. Artif. Intell. Stat. 15 (2011) 315--323. doi:10.1.1.208.6449."}],"event":{"number":"21","sponsor":["Univ of the West of England, University of the West of England","BytePress","Concordia University"],"acronym":"IDEAS 2017","name":"the 21st International Database Engineering & Applications Symposium","start":{"date-parts":[[2017,7,12]]},"location":"Bristol, United Kingdom","end":{"date-parts":[[2017,7,14]]}},"container-title":["Proceedings of the 21st International Database Engineering & Applications Symposium on - IDEAS 2017"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3105831.3105858","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/dl.acm.org\/ft_gateway.cfm?id=3105858&ftid=1896553&dwn=1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T03:30:04Z","timestamp":1750217404000},"score":1,"resource":{"primary":{"URL":"http:\/\/dl.acm.org\/citation.cfm?doid=3105831.3105858"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1145\/3105831.3105858","relation":{},"subject":[],"published":{"date-parts":[[2017]]}}}