{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T15:30:01Z","timestamp":1787844601305,"version":"build-2784847793"},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"vor","delay-in-days":365,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100006458","name":"Children's Hospital of Philadelphia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006458","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Biomedical Informatics"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1016\/j.jbi.2021.103984","type":"journal-article","created":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T03:06:38Z","timestamp":1641524798000},"page":"103984","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":132,"special_numbering":"C","title":["Classifying social determinants of health from unstructured electronic health records using deep learning-based natural language processing"],"prefix":"10.1016","volume":"127","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3281-5955","authenticated-orcid":false,"given":"Sifei","family":"Han","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1839-0604","authenticated-orcid":false,"given":"Robert F.","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingyun","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Russell","family":"Richie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7883-9637","authenticated-orcid":false,"given":"Haixia","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Tseng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Quan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neal","family":"Ryan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Brent","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6383-8471","authenticated-orcid":false,"given":"Fuchiang R.","family":"Tsui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jbi.2021.103984_b0005","unstructured":"Datto A. Social determinants of health. https:\/\/www.who.int\/health-topics\/social-determinants-of-health#tab=tab_(accessed 26 Jul 2021)."},{"key":"10.1016\/j.jbi.2021.103984_b0010","doi-asserted-by":"crossref","unstructured":"Halfon N, Larson K, Russ S. Why social determinants? Healthc Q 2010;14:8\u201320.","DOI":"10.12927\/hcq.2010.21979"},{"key":"10.1016\/j.jbi.2021.103984_b0015","doi-asserted-by":"crossref","unstructured":"Chen M, Tan X, Padman R. Social determinants of health in electronic health records and their impact on analysis and risk prediction: A systematic review. J Am Med Informatics Assoc 2020;27:1764\u201373.","DOI":"10.1093\/jamia\/ocaa143"},{"key":"10.1016\/j.jbi.2021.103984_b0020","unstructured":"Magnan S. Social determinants of health 101 for health care: five plus five. NAM Perspect 2017."},{"issue":"6","key":"10.1016\/j.jbi.2021.103984_b0025","first-page":"S8","article-title":"Moving upstream: how interventions that address the social determinants of health can improve health and reduce disparities","volume":"14","author":"Williams","year":"2008","journal-title":"J. Public Heal Manag. Pract. JPHMP"},{"key":"10.1016\/j.jbi.2021.103984_b0030","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40985-018-0094-7","article-title":"Screening for social determinants of health in clinical care: moving from the margins to the mainstream","volume":"39","author":"Andermann","year":"2018","journal-title":"Public Health Rev."},{"issue":"2","key":"10.1016\/j.jbi.2021.103984_b0035","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1111\/1475-6773.12670","article-title":"Hospital readmission and social risk factors identified from physician notes","volume":"53","author":"Navathe","year":"2018","journal-title":"Health Serv. Res."},{"key":"10.1016\/j.jbi.2021.103984_b0040","doi-asserted-by":"crossref","first-page":"103429","DOI":"10.1016\/j.jbi.2020.103429","article-title":"Maximizing the use of social and behavioural information from secondary care mental health electronic health records","volume":"107","author":"Goodday","year":"2020","journal-title":"J. Biomed. Inform."},{"key":"10.1016\/j.jbi.2021.103984_b0045","doi-asserted-by":"crossref","unstructured":"Bompelli A, Wang Y, Wan R, et al. Social determinants of health in the era of artificial intelligence with electronic health records: A systematic review. arXiv Prepr arXiv210204216 2021.","DOI":"10.34133\/2021\/9759016"},{"key":"10.1016\/j.jbi.2021.103984_b0050","doi-asserted-by":"crossref","unstructured":"Patra BG, Sharma MM, Vekaria V, et al. Extracting social determinants of health from electronic health records using natural language processing: a systematic review. J Am Med Informatics Assoc 2021.","DOI":"10.1093\/jamia\/ocab170"},{"key":"10.1016\/j.jbi.2021.103984_b0055","doi-asserted-by":"crossref","DOI":"10.1145\/2582112","article-title":"Examining the use, contents, and quality of free-text tobacco use documentation in the electronic health record","volume":"366","author":"Chen","year":"2014","journal-title":"AMIA Annual Symposium Proceedings."},{"issue":"3","key":"10.1016\/j.jbi.2021.103984_b0060","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1097\/MLR.0000000000000651","article-title":"A novel model for predicting rehospitalization risk incorporating physical function, cognitive status, and psychosocial support using natural language processing","volume":"55","author":"Greenwald","year":"2017","journal-title":"Med. Care"},{"key":"10.1016\/j.jbi.2021.103984_b0065","doi-asserted-by":"crossref","unstructured":"Chauhan S, Vig L, De Filippo De Grazia M, et al. A comparison of shallow and deep learning methods for predicting cognitive performance of stroke patients from MRI lesion images. Front Neuroinform 2019;13:53.","DOI":"10.3389\/fninf.2019.00053"},{"key":"10.1016\/j.jbi.2021.103984_b0070","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.inffus.2018.09.001","article-title":"Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection","volume":"49","author":"Kanjo","year":"2019","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.jbi.2021.103984_b0075","unstructured":"Feuerriegel S, Fehrer R. Improving decision analytics with deep learning: the case of financial disclosures. arXiv Prepr arXiv150801993 2015."},{"key":"10.1016\/j.jbi.2021.103984_b0080","doi-asserted-by":"crossref","first-page":"103631","DOI":"10.1016\/j.jbi.2020.103631","article-title":"Annotating social determinants of health using active learning, and characterizing determinants using neural event extraction","volume":"113","author":"Lybarger","year":"2021","journal-title":"J. Biomed. Inform."},{"issue":"01","key":"10.1016\/j.jbi.2021.103984_b0085","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1055\/s-0040-1702214","article-title":"Detecting social and behavioral determinants of health with structured and free-text clinical data","volume":"11","author":"Feller","year":"2020","journal-title":"Appl. Clin. Inform."},{"key":"10.1016\/j.jbi.2021.103984_b0090","unstructured":"SNOMED. No Title. https:\/\/www.snomed.org\/snomed-ct."},{"issue":"10","key":"10.1016\/j.jbi.2021.103984_b0095","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1001\/jama.1994.03520100096046","article-title":"DSM-IV: diagnostic and statistical manual of mental disorders","volume":"272","author":"Bell","year":"1994","journal-title":"JAMA"},{"issue":"1","key":"10.1016\/j.jbi.2021.103984_b0100","doi-asserted-by":"crossref","first-page":"W1","DOI":"10.7326\/M14-0698","article-title":"Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration","volume":"162","author":"Moons","year":"2015","journal-title":"Ann. Intern. Med."},{"issue":"1","key":"10.1016\/j.jbi.2021.103984_b0105","doi-asserted-by":"crossref","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":"10.1016\/j.jbi.2021.103984_b0110","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1001\/archpedi.152.10.952","article-title":"Substance abuse in children: Prediction, protection, and prevention","volume":"152","author":"Belcher","year":"1998","journal-title":"Arch. Pediatr. Adolesc. Med."},{"issue":"9","key":"10.1016\/j.jbi.2021.103984_b0115","doi-asserted-by":"crossref","first-page":"e0222611","DOI":"10.1371\/journal.pone.0222611","article-title":"Social and structural factors associated with substance use within the support network of adults living in precarious housing in a socially marginalized neighborhood of Vancouver, Canada","volume":"14","author":"Knerich","year":"2019","journal-title":"PLoS One"},{"issue":"20","key":"10.1016\/j.jbi.2021.103984_b0120","doi-asserted-by":"crossref","first-page":"1996","DOI":"10.1001\/jama.2019.16932","article-title":"Life expectancy and mortality rates in the United States, 1959\u20132017","volume":"322","author":"Woolf","year":"2019","journal-title":"JAMA"},{"key":"10.1016\/j.jbi.2021.103984_b0125","unstructured":"Devlin J, Chang M-W, Lee K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv Prepr arXiv181004805 2018."},{"key":"10.1016\/j.jbi.2021.103984_b0130","doi-asserted-by":"crossref","unstructured":"Kim Y. Convolutional neural networks for sentence classification. arXiv Prepr arXiv14085882 2014.","DOI":"10.3115\/v1\/D14-1181"},{"key":"10.1016\/j.jbi.2021.103984_b0135","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning C.D. Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2014. 1532\u201343.","DOI":"10.3115\/v1\/D14-1162"},{"issue":"8","key":"10.1016\/j.jbi.2021.103984_b0140","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":"10.1016\/j.jbi.2021.103984_b0145","unstructured":"Mart\\\u2019\\in\u223cAbadi, Ashish\u223cAgarwal, Paul\u223cBarham, et al. {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems. 2015.https:\/\/www.tensorflow.org\/."},{"issue":"3","key":"10.1016\/j.jbi.2021.103984_b0150","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1148\/radiol.2017171115","article-title":"Deep learning to classify radiology free-text reports","volume":"286","author":"Chen","year":"2018","journal-title":"Radiology"},{"key":"10.1016\/j.jbi.2021.103984_b0155","series-title":"2018 IEEE International Conference on Big Data (Big Data)","first-page":"2141","article-title":"Comparative study of CNN and LSTM based attention neural networks for aspect-level opinion mining","author":"Quan","year":"2018"},{"key":"10.1016\/j.jbi.2021.103984_b0160","unstructured":"Chollet F, others. Keras [Internet]. GitHub; 2015. Available from: https:\/\/github.com\/fchollet\/keras."},{"key":"10.1016\/j.jbi.2021.103984_b0165","series-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","first-page":"145","article-title":"On the stratification of multi-label data","author":"Sechidis","year":"2011"},{"key":"10.1016\/j.jbi.2021.103984_b0170","article-title":"Natural language processing with Python: analyzing text with the natural language toolkit","author":"Bird","year":"2009","journal-title":"\u2018 O\u2019Reilly Media, Inc\u2019."},{"key":"10.1016\/j.jbi.2021.103984_b0175","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.jbi.2021.103984_b0180","first-page":"2079","article-title":"On over-fitting in model selection and subsequent selection bias in performance evaluation","volume":"11","author":"Cawley","year":"2010","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.jbi.2021.103984_b0185","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.jbi.2021.103984_b0190","series-title":"Digital Healthcare Empowering Europeans","first-page":"924","article-title":"PyMedTermino: an open-source generic API for advanced terminology services","author":"Lamy","year":"2015"},{"key":"10.1016\/j.jbi.2021.103984_b0195","doi-asserted-by":"crossref","unstructured":"Stemerman R, Arguello J, Brice J, et al. Identification of social determinants of health using multi-label classification of electronic health record clinical notes. JAMIA Open 2021.","DOI":"10.1093\/jamiaopen\/ooaa069"},{"key":"10.1016\/j.jbi.2021.103984_b0200","unstructured":"Yin W, Kann K, Yu M, et al. Comparative study of CNN and RNN for natural language processing. arXiv Prepr arXiv170201923 2017."},{"key":"10.1016\/j.jbi.2021.103984_b0205","series-title":"Proceedings of the 31st international conference on neural information processing systems","first-page":"4768","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017"},{"key":"10.1016\/j.jbi.2021.103984_b0210","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","article-title":"BioBERT: a pre-trained biomedical language representation model for biomedical text mining","volume":"36","author":"Lee","year":"2020","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jbi.2021.103984_b0215","doi-asserted-by":"crossref","unstructured":"Alsentzer E, Murphy JR, Boag W, et al. Publicly available clinical BERT embeddings. arXiv Prepr arXiv190403323 2019.","DOI":"10.18653\/v1\/W19-1909"},{"key":"10.1016\/j.jbi.2021.103984_b0220","article-title":"Xlnet: Generalized autoregressive pretraining for language understanding","volume":"32","author":"Yang","year":"2019","journal-title":"Adv Neural Inf Process Syst"}],"container-title":["Journal of Biomedical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1532046421003130?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1532046421003130?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T22:51:42Z","timestamp":1761519102000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1532046421003130"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3]]},"references-count":44,"alternative-id":["S1532046421003130"],"URL":"https:\/\/doi.org\/10.1016\/j.jbi.2021.103984","relation":{},"ISSN":["1532-0464"],"issn-type":[{"value":"1532-0464","type":"print"}],"subject":[],"published":{"date-parts":[[2022,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Classifying social determinants of health from unstructured electronic health records using deep learning-based natural language processing","name":"articletitle","label":"Article Title"},{"value":"Journal of Biomedical Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jbi.2021.103984","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"simple-article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"103984"}}