{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T16:51:05Z","timestamp":1781974265158,"version":"3.54.5"},"reference-count":60,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Korean Government","award":["RS-2023-00273751"],"award-info":[{"award-number":["RS-2023-00273751"]}]},{"name":"Korean Government","award":["NRF-2023R1A2C1004919"],"award-info":[{"award-number":["NRF-2023R1A2C1004919"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/access.2023.3320179","type":"journal-article","created":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T17:51:29Z","timestamp":1695923489000},"page":"106334-106346","source":"Crossref","is-referenced-by-count":13,"title":["HSGA: A Hybrid LSTM-CNN Self-Guided Attention to Predict the Future Diagnosis From Discharge Narratives"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4761-562X","authenticated-orcid":false,"given":"Gaspard","family":"Harerimana","sequence":"first","affiliation":[{"name":"Department of Information Technology, Carnegie Mellon Universityy, Kigali, Rwanda"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gun Il","family":"Kim","sequence":"additional","affiliation":[{"name":"Graduate School of Information, Yonsei University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8373-1893","authenticated-orcid":false,"given":"Jong Wook","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Sangmyung University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3911-5935","authenticated-orcid":false,"given":"Beakcheol","family":"Jang","sequence":"additional","affiliation":[{"name":"Graduate School of Information, Yonsei University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/IAEAC47372.2019.8997969"},{"key":"ref57","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.01.078"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080834"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-16148-4_36"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-2766-4_12"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2916667"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1136\/jamia.2009.001560"},{"key":"ref52","first-page":"17","article-title":"MetaMap: Mapping text to the UMLS metathesaurus","author":"aronson","year":"2001","journal-title":"Proc AMIA Symp"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.11"},{"key":"ref55","article-title":"ClinicalBERT: Modeling clinical notes and predicting hospital readmission","author":"huang","year":"2019","journal-title":"arXiv 1904 05342"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00680"},{"key":"ref54","first-page":"1880","article-title":"Ensembles of NLP tools for data element extraction from clinical notes","author":"kuo","year":"2016","journal-title":"Proc Annu Symp"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-58053-z"},{"key":"ref16","first-page":"1","article-title":"Multitask learning and benchmarking with clinical time series data","volume":"6","author":"harutyunyan","year":"2019","journal-title":"Data Science Journal"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00120"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/607"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1055"},{"key":"ref50","first-page":"1","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"Proc NIPS"},{"key":"ref46","article-title":"Comparative study of CNN and RNN for natural language processing","author":"yin","year":"2017","journal-title":"arXiv 1702 01923"},{"key":"ref45","article-title":"A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification","author":"zhang","year":"2015","journal-title":"arXiv 1510 03820"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1006390"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0192360"},{"key":"ref42","article-title":"Effectiveness of LSTMs in predicting congestive heart failure onset","author":"mallya","year":"2019","journal-title":"arXiv 1902 02443"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM47256.2019.8983095"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-018-0029-1"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-019-0765-4"},{"key":"ref49","article-title":"Natural language processing (almost) from scratch","author":"collobert","year":"2011","journal-title":"arXiv 1103 0398"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W16-0103"},{"key":"ref7","article-title":"Neural machine translation by jointly learning to align and translate","author":"bahdanau","year":"2014","journal-title":"arXiv 1409 0473"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICEIEC.2019.8784662"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103114"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2019.06.020"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105321"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.05.024"},{"key":"ref40","first-page":"8180","article-title":"ICD coding from clinical text using multi-filter residual convolutional neural network","author":"li","year":"2019","journal-title":"Proc AAAI Conf Artif Intell AAAI Conf Artif Intell"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2021.104615"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1100"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2022.104714"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2021.103778"},{"key":"ref31","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"devlin","year":"2018","journal-title":"arXiv 1810 04805"},{"key":"ref30","article-title":"Progress notes classification and keyword extraction using attention-based deep learning models with BERT","author":"tang","year":"2019","journal-title":"arXiv 1910 05786"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICHI.2019.8904668"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2022.104956"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2019.104053"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-138-3-200302040-00007"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00127"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2023.105050"},{"key":"ref24","article-title":"Learning to diagnose with LSTM recurrent neural networks","author":"lipton","year":"2015","journal-title":"arXiv 1511 03677"},{"key":"ref23","first-page":"1","article-title":"MIMIC-III, a freely accessible critical care database","volume":"3","author":"johnson","year":"2016","journal-title":"Data Science Journal"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2022.104148"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098088"},{"key":"ref20","first-page":"1","article-title":"Attend and diagnose: Clinical time series analysis using attention models","author":"song","year":"2017","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref22","article-title":"The ‘K’ in K-fold cross validation","author":"anguita","year":"2012","journal-title":"Proc Eur Symp Artif Neural Netw"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/823"},{"key":"ref28","first-page":"1","article-title":"RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism","author":"choi","year":"2016","journal-title":"Proc NIPS"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3106637"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271701"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-77220-0_21"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10005208\/10266311.pdf?arnumber=10266311","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T18:20:07Z","timestamp":1698085207000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10266311\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":60,"URL":"https:\/\/doi.org\/10.1109\/access.2023.3320179","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}