{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T16:06:27Z","timestamp":1771517187000,"version":"3.50.1"},"reference-count":37,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T00:00:00Z","timestamp":1572566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T00:00:00Z","timestamp":1572566400000},"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":[[2019,11]]},"DOI":"10.1109\/bibm47256.2019.8983378","type":"proceedings-article","created":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T02:49:51Z","timestamp":1581043791000},"page":"1141-1148","source":"Crossref","is-referenced-by-count":18,"title":["Disease Prediction Model Based on BiLSTM and Attention Mechanism"],"prefix":"10.1109","author":[{"given":"Yang","family":"Yang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University,Jinan,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangwei","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University,Jinan,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cun","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University,Jinan,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35"},{"key":"ref32","author":"escudi\u00e9","year":"2018","journal-title":"Deep Representation for Patient Visits from Electronic Health Records[J]"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocw112"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S16-1198"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICFHR.2014.55"},{"key":"ref36","author":"zaremba","year":"2014","journal-title":"Recurrent Neural Network Regularization"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1542\/peds.114.2.S2.555"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(10)62226-X"},{"key":"ref10","author":"murphy","year":"2012","journal-title":"Machine Learning A Probabilistic Perspective"},{"key":"ref11","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref12","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":"ref13","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1082"},{"key":"ref14","first-page":"473","author":"jagannatha","year":"2016","journal-title":"Bidirectional Recurrent Neural Networks for Medical Event Detection in Electronic Health Records"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2399298"},{"key":"ref16","first-page":"45","author":"choi","year":"2016","journal-title":"Medical concept representation learning from electronic health records and its application on heart failure prediction"},{"key":"ref17","first-page":"1","author":"nguyen","year":"2016","journal-title":"Deepr A Convolutional Net for Medical Records"},{"key":"ref18","first-page":"1","author":"choi","year":"2015","journal-title":"Doctor AI Predicting clinical events via recurrent neural networks"},{"key":"ref19","first-page":"1","author":"choi","year":"2016","journal-title":"Multi-layer representation learning for medical concepts"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S16-1198"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1038\/nrg3208","article-title":"Translational genetics: Mining electronic health records: towards better research applications and clinical care","volume":"13","author":"ensen","year":"2012","journal-title":"Nature Rev Genet"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390294"},{"key":"ref3","author":"j e","year":"2016","journal-title":"Electronic Health Record Adoption and Use among Office-based Physicians in the U S by State 2015 National Electronic Health Records Survey"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2011-000163"},{"key":"ref29","article-title":"Learning to Diagnose with LSTM Recurrent Neural Networks[J]","author":"lipton","year":"2015","journal-title":"Computer Science"},{"key":"ref5","first-page":"128","article-title":"Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research","volume":"47","author":"meystre","year":"2008","journal-title":"IMIA Yearbook Med Informat Methods Inf Med"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2011.05.004"},{"key":"ref7","first-page":"192","article-title":"Predicting Patient Trajectory of Physiological Data using Temporal Trends in Similar Patients: A System for Near-Term Prognostics","author":"ebadollahi","year":"0","journal-title":"AMIA Annu Symp Proc"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2016.7591352"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1197\/jamia.M2170","article-title":"Medication-related Clinical Decision Support in Computerized Provider Order Entry Systems: A Review","volume":"14","author":"burns","year":"2007","journal-title":"Journal of the American Medical Informatics Association"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-publhealth-031914-122747"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2015.01.012"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1186\/s12887-016-0592-z"},{"key":"ref21","article-title":"Deep learning for health informatics","author":"ravi","year":"2016","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"ref24","author":"greff","year":"2015","journal-title":"LSTM A Search Space Odyssey"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-bioeng-071516-044442"},{"key":"ref26","first-page":"41","article-title":"Learning Low-Dimensional Representations of Medical Concepts Methods Background","author":"choi","year":"0","journal-title":"AMIA Clinical Research Informatics Summit"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICHI.2015.58"}],"event":{"name":"2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","location":"San Diego, CA, USA","start":{"date-parts":[[2019,11,18]]},"end":{"date-parts":[[2019,11,21]]}},"container-title":["2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8965270\/8982928\/08983378.pdf?arnumber=8983378","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T19:23:49Z","timestamp":1756754629000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8983378\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/bibm47256.2019.8983378","relation":{},"subject":[],"published":{"date-parts":[[2019,11]]}}}