{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T14:12:20Z","timestamp":1786371140958,"version":"3.56.0"},"reference-count":123,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2018,6,8]],"date-time":"2018-06-08T00:00:00Z","timestamp":1528416000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-#1418511"],"award-info":[{"award-number":["IIS-#1418511"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CCF-#1533768"],"award-info":[{"award-number":["CCF-#1533768"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1R01MD011682-01"],"award-info":[{"award-number":["1R01MD011682-01"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R56HL138415"],"award-info":[{"award-number":["R56HL138415"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Children\u2019s Healthcare of Atlanta"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>To conduct a systematic review of deep learning models for electronic health record (EHR) data, and illustrate various deep learning architectures for analyzing different data sources and their target applications. We also highlight ongoing research and identify open challenges in building deep learning models of EHRs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Design\/method<\/jats:title><jats:p>We searched PubMed and Google Scholar for papers on deep learning studies using EHR data published between January 1, 2010, and January 31, 2018. We summarize them according to these axes: types of analytics tasks, types of deep learning model architectures, special challenges arising from health data and tasks and their potential solutions, as well as evaluation strategies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We surveyed and analyzed multiple aspects of the 98 articles we found and identified the following analytics tasks: disease detection\/classification, sequential prediction of clinical events, concept embedding, data augmentation, and EHR data privacy. We then studied how deep architectures were applied to these tasks. We also discussed some special challenges arising from modeling EHR data and reviewed a few popular approaches. Finally, we summarized how performance evaluations were conducted for each task.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>Despite the early success in using deep learning for health analytics applications, there still exist a number of issues to be addressed. We discuss them in detail including data and label availability, the interpretability and transparency of the model, and ease of deployment.<\/jats:p><\/jats:sec>","DOI":"10.1093\/jamia\/ocy068","type":"journal-article","created":{"date-parts":[[2018,5,9]],"date-time":"2018-05-09T03:18:48Z","timestamp":1525835928000},"page":"1419-1428","source":"Crossref","is-referenced-by-count":678,"title":["Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3869-6942","authenticated-orcid":false,"given":"Cao","family":"Xiao","sequence":"first","affiliation":[{"name":"AI for Healthcare, IBM Research, Cambridge, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edward","family":"Choi","sequence":"additional","affiliation":[{"name":"School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jimeng","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2018,6,8]]},"reference":[{"key":"2020110612235098100_ocy068-B1","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.artmed.2016.05.005","article-title":"Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods","volume":"71","author":"Richesson","year":"2016","journal-title":"Artif Intell Med"},{"issue":"7553","key":"2020110612235098100_ocy068-B2","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"issue":"22","key":"2020110612235098100_ocy068-B3","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"issue":"7639","key":"2020110612235098100_ocy068-B4","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"issue":"12","key":"2020110612235098100_ocy068-B5","doi-asserted-by":"crossref","first-page":"i121","DOI":"10.1093\/bioinformatics\/btu277","article-title":"Deep learning of the tissue-regulated splicing code","volume":"30","author":"Leung","year":"2014","journal-title":"Bioinformatics"},{"issue":"6218","key":"2020110612235098100_ocy068-B6","doi-asserted-by":"crossref","first-page":"1254806","DOI":"10.1126\/science.1254806","article-title":"RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease","volume":"347","author":"Xiong","year":"2015","journal-title":"Science"},{"key":"2020110612235098100_ocy068-B7","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med Image Anal"},{"issue":"7","key":"2020110612235098100_ocy068-B8","doi-asserted-by":"crossref","first-page":"878.","DOI":"10.15252\/msb.20156651","article-title":"Deep learning for computational biology","volume":"12","author":"Angermueller","year":"2016","journal-title":"Mol Syst Biol"},{"key":"2020110612235098100_ocy068-B9","article-title":"Opportunities and obstacles for deep learning in biology and medicine","author":"Ching","year":"2017","journal-title":"bioRxiv"},{"key":"2020110612235098100_ocy068-B10","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.jbi.2016.10.007","article-title":"Semi-supervised learning of the electronic health record for phenotype stratification","volume":"64","author":"Beaulieu-Jones","year":"2016","journal-title":"J Biomed Inform"},{"key":"2020110612235098100_ocy068-B11","author":"Baytas","year":"2017"},{"key":"2020110612235098100_ocy068-B12","first-page":"432","author":"Cheng","year":"2016"},{"key":"2020110612235098100_ocy068-B13","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.compbiomed.2017.08.015","article-title":"Learning representations for the early detection of sepsis with deep neural networks","volume":"89","author":"Kam","year":"2017","journal-title":"Comput Biol Med"},{"key":"2020110612235098100_ocy068-B14","author":"Che","year":"2017"},{"key":"2020110612235098100_ocy068-B15","article-title":"Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals","author":"Acharya","year":"2017","journal-title":"Comput Biol Med"},{"key":"2020110612235098100_ocy068-B16","doi-asserted-by":"crossref","first-page":"160035.","DOI":"10.1038\/sdata.2016.35","article-title":"MIMIC-III, a freely accessible critical care database","volume":"3","author":"Johnson","year":"2016","journal-title":"Sci Data"},{"key":"2020110612235098100_ocy068-B17","article-title":"Grounded recurrent neural networks","author":"Vani","year":"2017","journal-title":"arXiv [Stat.ML]"},{"key":"2020110612235098100_ocy068-B18","article-title":"Explainable Prediction of Medical Codes from Clinical Text","author":"Mullenbach","year":"2018","journal-title":"arXiv [Cs.CL]"},{"key":"2020110612235098100_ocy068-B19","article-title":"Towards Automated ICD Coding Using Deep Learning","author":"Shi","year":"2017","journal-title":"arXiv [Cs.CL]"},{"key":"2020110612235098100_ocy068-B20","article-title":"Multi-Label Classification of Patient Notes a Case Study on ICD Code Assignment","author":"Baumel","year":"2017","journal-title":"arXiv [Cs.CL]"},{"key":"2020110612235098100_ocy068-B21","first-page":"195","volume-title":"Advances in Big Data","author":"Yoon","year":"2016"},{"key":"2020110612235098100_ocy068-B22","article-title":"Deep learning for automated extraction of primary sites from cancer pathology reports","author":"Qiu","year":"2017","journal-title":"IEEE J Biomed Health Inform"},{"key":"2020110612235098100_ocy068-B23","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1093\/jamia\/ocw112","article-title":"Using recurrent neural network models for early detection of heart failure onset","volume":"24","author":"Choi","year":"2017","journal-title":"J Am Med Inform Assoc"},{"key":"2020110612235098100_ocy068-B24","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.jbi.2015.05.016","article-title":"A comparison of models for predicting early hospital readmissions","volume":"56","author":"Futoma","year":"2015","journal-title":"J Biomed Inform"},{"key":"2020110612235098100_ocy068-B25","article-title":"Scalable and accurate deep learning for electronic health records","author":"Rajkomar","year":"2018","journal-title":"arXiv [Cs.CY]"},{"key":"2020110612235098100_ocy068-B26","first-page":"301","article-title":"Doctor AI: predicting clinical events via recurrent neural networks","volume":"56","author":"Choi","year":"2016","journal-title":"JMLR Workshop Conf Proc"},{"key":"2020110612235098100_ocy068-B27","author":"Bajor","year":"2016"},{"key":"2020110612235098100_ocy068-B28","first-page":"1315","author":"Zhang","year":"2017"},{"key":"2020110612235098100_ocy068-B29","author":"Choi","year":"2016"},{"issue":"1","key":"2020110612235098100_ocy068-B30","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":"Sci Rep"},{"key":"2020110612235098100_ocy068-B31","article-title":"Comparing Rule-Based and Deep Learning Models for Patient Phenotyping","author":"Gehrmann","year":"2017","journal-title":"arXiv [Cs.CL]"},{"issue":"1","key":"2020110612235098100_ocy068-B32","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1186\/s12911-017-0518-1","article-title":"Word2Vec inversion and traditional text classifiers for phenotyping lupus","volume":"17","author":"Turner","year":"2017","journal-title":"BMC Med Inform Decis Mak"},{"key":"2020110612235098100_ocy068-B33","author":"Che","year":"2017"},{"key":"2020110612235098100_ocy068-B34","author":"Choi","year":"2017"},{"key":"2020110612235098100_ocy068-B35","article-title":"Generative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories","author":"Yahi","year":"2017","journal-title":"arXiv [Cs.LG]"},{"issue":"3","key":"2020110612235098100_ocy068-B36","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1093\/jamia\/ocw156","article-title":"De-identification of patient notes with recurrent neural networks","volume":"24","author":"Dernoncourt","year":"2017","journal-title":"J Am Med Inform Assoc"},{"key":"2020110612235098100_ocy068-B37","doi-asserted-by":"crossref","first-page":"S34","DOI":"10.1016\/j.jbi.2017.05.023","article-title":"De-identification of clinical notes via recurrent neural network and conditional random field","volume":"75","author":"Liu","year":"2017","journal-title":"J Biomed Inform"},{"key":"2020110612235098100_ocy068-B38","first-page":"1799","volume-title":"Advances in Neural Information Processing Systems 27","author":"Tompson","year":"2014"},{"key":"2020110612235098100_ocy068-B39","first-page":"3104","volume-title":"Advances in Neural Information Processing Systems 27","author":"Sutskever","year":"2014"},{"issue":"6","key":"2020110612235098100_ocy068-B40","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","article-title":"Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups","volume":"29","author":"Hinton","year":"2012","journal-title":"IEEE Signal Process Mag"},{"key":"2020110612235098100_ocy068-B41","first-page":"3504","volume-title":"Advances in Neural Information Processing Systems 29","author":"Choi","year":"2016"},{"key":"2020110612235098100_ocy068-B42","author":"Choi","year":"2017"},{"key":"2020110612235098100_ocy068-B43","author":"Ayyar"},{"key":"2020110612235098100_ocy068-B44","article-title":"Learning to diagnose with LSTM recurrent neural networks","author":"Lipton","year":"2015","journal-title":"arXiv [Cs.LG]"},{"key":"2020110612235098100_ocy068-B45","author":"Ma","year":"2017"},{"key":"2020110612235098100_ocy068-B46","first-page":"112","article-title":"Deep learning from EEG reports for inferring underspecified information","volume":"2017","author":"Goodwin","year":"2017","journal-title":"AMIA Jt Summits Transl Sci Proc"},{"key":"2020110612235098100_ocy068-B47","article-title":"Finding Algebraic Structure of Care in Time: A Deep Learning Approach","author":"Nguyen","year":"2017","journal-title":"arXiv [Cs.LG]"},{"key":"2020110612235098100_ocy068-B48","first-page":"473","author":"Jagannatha","year":"2016"},{"key":"2020110612235098100_ocy068-B49","first-page":"856","article-title":"Structured prediction models for RNN based sequence labeling in clinical text","volume":"2016","author":"Jagannatha","year":"2016","journal-title":"Proc Conf Empir Methods Nat Lang Process"},{"key":"2020110612235098100_ocy068-B50","author":"Veli\u010dkovi\u0107","year":"2017"},{"key":"2020110612235098100_ocy068-B51","author":"Thodoroff","year":"2016"},{"key":"2020110612235098100_ocy068-B52","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.jbi.2017.07.006","article-title":"Recurrent neural networks for classifying relations in clinical notes","volume":"72","author":"Luo","year":"2017","journal-title":"J Biomed Inform"},{"key":"2020110612235098100_ocy068-B53","article-title":"Medical Diagnosis From Laboratory Tests by Combining Generative and Discriminative Learning","author":"Zhang","year":"2017","journal-title":"arXiv [Cs.AI]"},{"key":"2020110612235098100_ocy068-B54","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1007\/978-3-319-31750-2_3","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"Pham","year":"2016"},{"key":"2020110612235098100_ocy068-B55","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.jbi.2017.04.001","article-title":"Predicting healthcare trajectories from medical records: a deep learning approach","volume":"69","author":"Pham","year":"2017","journal-title":"J Biomed Inform"},{"key":"2020110612235098100_ocy068-B56","first-page":"93","author":"Esteban","year":"2016"},{"key":"2020110612235098100_ocy068-B57","article-title":"Clinical Intervention Prediction and Understanding Using Deep Networks","author":"Suresh","year":"2017","journal-title":"arXiv [Cs.LG]"},{"key":"2020110612235098100_ocy068-B58","article-title":"An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection","author":"Futoma","year":"2017","journal-title":"arXiv [Stat.ML]"},{"key":"2020110612235098100_ocy068-B59","article-title":"Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier","author":"Futoma","year":"2017","journal-title":"arXiv [Stat.ML]"},{"key":"2020110612235098100_ocy068-B60","first-page":"164","author":"Yang","year":"2017"},{"key":"2020110612235098100_ocy068-B61","author":"Liu","year":"2017"},{"key":"2020110612235098100_ocy068-B62","author":"Razavian","year":"2016"},{"key":"2020110612235098100_ocy068-B63","article-title":"The Use of Autoencoders for Discovering Patient Phenotypes","author":"Suresh","year":"2017","journal-title":"arXiv [Cs.LG]"},{"key":"2020110612235098100_ocy068-B64","author":"Che","year":"2017"},{"key":"2020110612235098100_ocy068-B65","article-title":"Learning Effective Representations from Clinical Notes","author":"Dubois","year":"2017","journal-title":"arXiv [Stat.ML]"},{"key":"2020110612235098100_ocy068-B66","first-page":"886","author":"Jia","year":"2017"},{"key":"2020110612235098100_ocy068-B67","author":"Lipton","year":"2016"},{"key":"2020110612235098100_ocy068-B68","author":"Potes","year":"2016"},{"key":"2020110612235098100_ocy068-B69","article-title":"Multi-Label Learning from Medical Plain Text with Convolutional Residual Models","author":"Zhang","year":"2018","journal-title":"arXiv [Stat.ML]"},{"key":"2020110612235098100_ocy068-B70","article-title":"Temporal Convolutional Neural Networks for Diagnosis from Lab Tests","author":"Razavian","year":"2015","journal-title":"arXiv [Cs.LG]"},{"key":"2020110612235098100_ocy068-B71","article-title":"DeepIED: an epileptic discharge detector for EEG-fMRI based on deep learning","author":"Hao","journal-title":"Neuroimage Clin"},{"key":"2020110612235098100_ocy068-B72","article-title":"Predicting Discharge Medications at Admission Time Based on Deep Learning","author":"Yang","year":"2017","journal-title":"arXiv [Cs.CL]"},{"issue":"1","key":"2020110612235098100_ocy068-B73","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/JBHI.2016.2633963","article-title":"$\\mathtt {Deepr}$: a convolutional net for medical records","volume":"21","author":"Nguyen","year":"2017","journal-title":"IEEE J Biomed Health Inform"},{"key":"2020110612235098100_ocy068-B74","author":"Zhu","year":"2016"},{"key":"2020110612235098100_ocy068-B75","article-title":"Exploiting Convolutional Neural Network for Risk Prediction with Medical Feature Embedding","author":"Che","year":"2017","journal-title":"arXiv [Cs.LG]"},{"issue":"1","key":"2020110612235098100_ocy068-B76","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1093\/jamia\/ocx090","article-title":"Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes","volume":"25","author":"Luo","year":"2018","journal-title":"J Am Med Inform Assoc"},{"key":"2020110612235098100_ocy068-B77","article-title":"Neural Document Embeddings for Intensive Care Patient Mortality Prediction","author":"Grnarova","year":"2016","journal-title":"arXiv [Cs.CL]"},{"key":"2020110612235098100_ocy068-B78","author":"Suo","year":"2016"},{"key":"2020110612235098100_ocy068-B79","author":"Yuan","year":"2017"},{"key":"2020110612235098100_ocy068-B80","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.jbi.2017.11.003","article-title":"Predicting age by mining electronic medical records with deep learning characterizes differences between chronological and physiological age","volume":"76","author":"Wang","year":"2017","journal-title":"J Biomed Inform"},{"key":"2020110612235098100_ocy068-B81","article-title":"A regularized deep learning approach for clinical risk prediction of acute coronary syndrome using electronic health records","author":"Huang","year":"2017","journal-title":"IEEE Trans Biomed Eng"},{"key":"2020110612235098100_ocy068-B82","author":"Che","year":"2015"},{"issue":"6","key":"2020110612235098100_ocy068-B83","doi-asserted-by":"crossref","first-page":"e66341.","DOI":"10.1371\/journal.pone.0066341","article-title":"Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data","volume":"8","author":"Lasko","year":"2013","journal-title":"PLoS One"},{"issue":"7","key":"2020110612235098100_ocy068-B84","doi-asserted-by":"crossref","first-page":"237","DOI":"10.14257\/ijhit.2016.9.7.22","article-title":"Clinical relation extraction with deep learning","volume":"9","author":"Lv","year":"2016","journal-title":"Int J Hybrid Inform Technol"},{"key":"2020110612235098100_ocy068-B85","first-page":"191","article-title":"Applying deep learning on electronic health records in Swedish to predict healthcare-associated infections","volume":"2016","author":"Jacobson","year":"2016","journal-title":"ACL"},{"key":"2020110612235098100_ocy068-B86","first-page":"A16708","volume-title":"Circulation","author":"Ulloa Cerna","year":"2017"},{"key":"2020110612235098100_ocy068-B87","article-title":"Learning compressed representations of blood samples time series with missing data","author":"Bianchi","year":"2017","journal-title":"arXiv [Cs.NE]"},{"key":"2020110612235098100_ocy068-B88","author":"Yuan","year":"2017"},{"key":"2020110612235098100_ocy068-B89","article-title":"Disease Prediction from Electronic Health Records Using Generative Adversarial Networks","author":"Hwang","year":"2017","journal-title":"arXiv [Cs.LG]"},{"key":"2020110612235098100_ocy068-B90","first-page":"207","article-title":"Missing data imputation in the electronic health record using deeply learned autoencoders","volume":"22","author":"Beaulieu-Jones","year":"2017","journal-title":"Pac Symp Biocomput"},{"key":"2020110612235098100_ocy068-B91","first-page":"371","article-title":"Interpretable deep models for ICU outcome prediction","volume":"2016","author":"Che","year":"2016","journal-title":"AMIA Annu Symp Proc"},{"key":"2020110612235098100_ocy068-B92","author":"Liang","year":"2014"},{"key":"2020110612235098100_ocy068-B93","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.jbi.2015.08.013","article-title":"Identifying adverse drug event information in clinical notes with distributional semantic representations of context","volume":"57","author":"Henriksson","year":"2015","journal-title":"J Biomed Inform"},{"key":"2020110612235098100_ocy068-B94","author":"Du","year":"2016"},{"key":"2020110612235098100_ocy068-B95","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.jbi.2015.01.012","article-title":"Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)","volume":"54","author":"Tran","year":"2015","journal-title":"J Biomed Inform"},{"key":"2020110612235098100_ocy068-B96","first-page":"145","article-title":"Automated disease cohort selection using word embeddings from electronic health records","volume":"23","author":"Glicksberg","year":"2018","journal-title":"Pac Symp Biocomput"},{"key":"2020110612235098100_ocy068-B97","author":"Prakash","year":"2017"},{"key":"2020110612235098100_ocy068-B98","article-title":"Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs","author":"Esteban","year":"2017","journal-title":"arXiv [Stat.ML]"},{"issue":"8","key":"2020110612235098100_ocy068-B99","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput"},{"key":"2020110612235098100_ocy068-B100","article-title":"Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation","author":"Cho","year":"2014","journal-title":"arXiv [Cs.CL]"},{"key":"2020110612235098100_ocy068-B101","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1007\/978-3-319-42297-8_74","volume-title":"Intelligent Computing Methodologies","author":"Lin","year":"2016"},{"key":"2020110612235098100_ocy068-B102","author":"Yan","year":"2016"},{"key":"2020110612235098100_ocy068-B103","article-title":"SLEEPNET: Automated Sleep Staging System via Deep Learning","author":"Biswal","year":"2017","journal-title":"arXiv [Cs.LG]"},{"key":"2020110612235098100_ocy068-B104","first-page":"41","article-title":"Learning low-dimensional representations of medical concepts","volume":"2016","author":"Choi","year":"2016","journal-title":"AMIA Jt Summits Transl Sci Proc"},{"key":"2020110612235098100_ocy068-B105","first-page":"2672","volume-title":"Advances in Neural Information Processing Systems 27","author":"Goodfellow","year":"2014"},{"key":"2020110612235098100_ocy068-B106","author":"Choi","year":"2016"},{"key":"2020110612235098100_ocy068-B107","author":"Jagannatha","year":"2016"},{"key":"2020110612235098100_ocy068-B108","article-title":"Deep Counterfactual Networks with Propensity-Dropout","author":"Alaa","year":"2017","journal-title":"arXiv [Cs.LG]"},{"key":"2020110612235098100_ocy068-B109","author":"Nagpal"},{"key":"2020110612235098100_ocy068-B110","first-page":"1","volume-title":"J Mach Learn Res","author":"Henao","year":"2016"},{"key":"2020110612235098100_ocy068-B111","author":"Dubois","year":"2017"},{"key":"2020110612235098100_ocy068-B112","article-title":"The Mythos of Model Interpretability","author":"Lipton","year":"2016","journal-title":"arXiv [Cs.LG]"},{"key":"2020110612235098100_ocy068-B113","article-title":"Understanding Black-box Predictions via Influence Functions","author":"Koh","year":"2017","journal-title":"arXiv [Stat.ML]"},{"key":"2020110612235098100_ocy068-B114","article-title":"Neural Machine Translation by Jointly Learning to Align and Translate","author":"Bahdanau","year":"2014","journal-title":"arXiv [Cs.CL]"},{"key":"2020110612235098100_ocy068-B115","article-title":"Distilling Knowledge from Deep Networks with Applications to Healthcare Domain","author":"Che","year":"2015","journal-title":"arXiv [Stat.ML]"},{"key":"2020110612235098100_ocy068-B116","article-title":"Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks","author":"Bradshaw","year":"2017","journal-title":"arXiv [Stat.ML]"},{"key":"2020110612235098100_ocy068-B117","article-title":"Partial Transfer Learning with Selective Adversarial Networks","author":"Cao","year":"2017","journal-title":"arXiv [Cs.LG]"},{"key":"2020110612235098100_ocy068-B118","author":"Johansson","year":"2016"},{"key":"2020110612235098100_ocy068-B119","article-title":"Predicting Adolescent Suicide Attempts with Neural Networks","author":"Bhat","year":"2017","journal-title":"arXiv [Stat.ML]"},{"key":"2020110612235098100_ocy068-B120","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1007\/978-3-319-30671-1_66","volume-title":"Advances in Information Retrieval","author":"Miotto","year":"2016"},{"key":"2020110612235098100_ocy068-B121","author":"Avati","year":"2017"},{"key":"2020110612235098100_ocy068-B122","author":"Rajkomar","year":"2018"},{"issue":"141","key":"2020110612235098100_ocy068-B123","doi-asserted-by":"crossref","first-page":"20170387","DOI":"10.1098\/rsif.2017.0387","article-title":"Opportunities and obstacles for deep learning in biology and medicine","volume":"15","author":"Ching","year":"2018","journal-title":"J R Soc Interface"}],"container-title":["Journal of the American Medical Informatics Association"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/jamia\/article-pdf\/25\/10\/1419\/34150605\/ocy068.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/jamia\/article-pdf\/25\/10\/1419\/34150605\/ocy068.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T21:27:01Z","timestamp":1661203621000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jamia\/article\/25\/10\/1419\/5035024"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,8]]},"references-count":123,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2018,6,8]]},"published-print":{"date-parts":[[2018,10,1]]}},"URL":"https:\/\/doi.org\/10.1093\/jamia\/ocy068","relation":{},"ISSN":["1067-5027","1527-974X"],"issn-type":[{"value":"1067-5027","type":"print"},{"value":"1527-974X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2018,10]]},"published":{"date-parts":[[2018,6,8]]}}}