{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T00:12:16Z","timestamp":1781741536951,"version":"3.54.5"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"1","funder":[{"name":"National Library of Medicine, National Institutes of Health","award":["5R00LM011575"],"award-info":[{"award-number":["5R00LM011575"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2017,12]]},"DOI":"10.1186\/s12911-017-0556-8","type":"journal-article","created":{"date-parts":[[2017,12,1]],"date-time":"2017-12-01T02:57:25Z","timestamp":1512097045000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":135,"title":["Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach"],"prefix":"10.1186","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2232-0390","authenticated-orcid":false,"given":"Wei-Hung","family":"Weng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kavishwar B.","family":"Wagholikar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexa T.","family":"McCray","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Szolovits","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henry C.","family":"Chueh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2017,12,1]]},"reference":[{"issue":"1","key":"556_CR1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/505282.505283","volume":"31","author":"F Sebastiani","year":"2002","unstructured":"Sebastiani F. Machine learning in automated text categorization. ACM Computing Surveys (CSUR). 2002;31(1):1\u201347.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"556_CR2","unstructured":"Charles D. Adoption of Electronic Health Record Systems among U.S. Non-Federal Acute Care Hospitals: 2008\u20132014. 2015. https:\/\/www.healthit.gov\/sites\/default\/files\/data-brief\/2014HospitalAdoptionDataBrief.pdf . Accessed 18 Feb 2017."},{"key":"556_CR3","unstructured":"Bernhardt PJ, Humphrey SM, Rindflesch TC. Determining prominent subdomains in medicine. AMIA Annu Symp Proc. 2005:46\u201350."},{"key":"556_CR4","first-page":"1","volume":"3","author":"J Yuan","year":"2017","unstructured":"Yuan J. Autism Spectrum disorder detection from semi-structured and unstructured medical data. EURASIP J Bioinforma Syst Biol. 2017;3:1\u20139.","journal-title":"EURASIP J Bioinforma Syst Biol"},{"key":"556_CR5","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.jbi.2016.10.008","volume":"64","author":"S Kocbek","year":"2016","unstructured":"Kocbek S, Cavedon L, Martinez D, Bain C, Mac Manus C, Haffari G, et al. Text mining electronic hospital records to automatically classify admissions against disease: measuring the impact of linking data sources. J Biomed Inform. 2016;64:158\u201367.","journal-title":"J Biomed Inform"},{"key":"556_CR6","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1016\/j.eswa.2013.08.047","volume":"41","author":"JJG Adeva","year":"2014","unstructured":"Adeva JJG, Atxa JMP, Carrillo MU, Zengotitabengoa EA. Automatic text classification to support systematic reviews in medicine. Expert Syst Appl. 2014;41:1498\u2013508.","journal-title":"Expert Syst Appl"},{"issue":"8","key":"556_CR7","doi-asserted-by":"crossref","first-page":"e69932","DOI":"10.1371\/journal.pone.0069932","volume":"8","author":"C Lin","year":"2013","unstructured":"Lin C, Karlson EW, Canhao H, Miller TA, Dligach D, Chen PJ, et al. Automatic prediction of rheumatoid arthritis disease activity from the electronic medical records. PLoS One. 2013;8(8):e69932\u201310.","journal-title":"PLoS One"},{"issue":"8","key":"556_CR8","doi-asserted-by":"crossref","first-page":"e0136651","DOI":"10.1371\/journal.pone.0136651","volume":"10","author":"KP Liao","year":"2015","unstructured":"Liao KP, Ananthakrishnan AN, Kumar V, et al. Methods to develop an electronic medical record phenotype algorithm to compare the risk of coronary artery disease across 3 chronic disease cohorts. PLoS One. 2015;10(8):e0136651.","journal-title":"PLoS One"},{"issue":"8","key":"556_CR9","doi-asserted-by":"crossref","first-page":"e0136341","DOI":"10.1371\/journal.pone.0136341","volume":"10","author":"TH McCoy","year":"2015","unstructured":"McCoy TH, Castro VM, Cagan A, et al. Sentiment measured in hospital discharge notes is associated with readmission and mortality risk: an electronic health record study. PLoS One. 2015;10(8):e0136341.","journal-title":"PLoS One"},{"issue":"5","key":"556_CR10","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1136\/amiajnl-2014-002694","volume":"21","author":"BJ Marafino","year":"2014","unstructured":"Marafino BJ, Davies JM, Bardach NS, Dean ML, Dudley RA. N-Gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit. J Am Med Inform Assoc. 2014;21(5):871\u20135.","journal-title":"J Am Med Inform Assoc"},{"issue":"12","key":"556_CR11","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1016\/j.ijmedinf.2012.12.005","volume":"83","author":"RJ Byrd","year":"2014","unstructured":"Byrd RJ, Steinhubl SR, Sun J, et al. Automatic identification of heart failure diagnostic criteria, using text analysis of clinical notes from electronic health records. Int J Med Inform. 2014;83(12):983\u201392.","journal-title":"Int J Med Inform"},{"key":"556_CR12","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.jbi.2014.11.002","volume":"53","author":"A Sarker","year":"2015","unstructured":"Sarker A, Gonzalez G. Portable automatic text classification for adverse drug reaction detection via multi-corpus training. J Biomed Inform. 2015;53:196\u2013207.","journal-title":"J Biomed Inform"},{"issue":"10","key":"556_CR13","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1007\/s40264-014-0218-z","volume":"37","author":"R Harpaz","year":"2014","unstructured":"Harpaz R, Callahan A, Tamang S, Low Y, Odgers D, Finlayson S, et al. Text Mining for Adverse Drug Events: the promise, challenges, and state of the art. Drug Saf. 2014;37(10):777\u201390.","journal-title":"Drug Saf"},{"issue":"5","key":"556_CR14","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1136\/amiajnl-2013-002463","volume":"21","author":"W ST","year":"2014","unstructured":"ST W, Juhn YJ, Sohn S, Liu H. Patient-level temporal aggregation for text-based asthma status ascertainment. J Am Med Inform Assoc. 2014;21(5):876\u201384.","journal-title":"J Am Med Inform Assoc"},{"key":"556_CR15","first-page":"2428","volume-title":"Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers","author":"X Wang","year":"2016","unstructured":"Wang X, Jiang W, Luo Z. Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers; 2016. p. 2428\u201337."},{"key":"556_CR16","doi-asserted-by":"crossref","first-page":"e87555","DOI":"10.1371\/journal.pone.0087555","volume":"9","author":"R Cohen","year":"2014","unstructured":"Cohen R, Aviram I, Elhadad M, Elhadad N. Redundancy-aware topic modeling for patient record notes. PLoS One. 2014;9:e87555.","journal-title":"PLoS One"},{"issue":"2","key":"556_CR17","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1111\/acem.12859","volume":"23","author":"K Yadav","year":"2016","unstructured":"Yadav K, Sarioglu E, Choi H-A, Cartwright WBIV, Hinds PS, Chamberlain JM. Automated outcome classification of computed tomography imaging reports for pediatric traumatic brain injury. Acad Emerg Med. 2016;23(2):171\u20138.","journal-title":"Acad Emerg Med"},{"issue":"Suppl 1","key":"556_CR18","doi-asserted-by":"crossref","first-page":"S2","DOI":"10.1186\/2041-1480-3-S1-S2","volume":"3","author":"G Tsatsaronis","year":"2012","unstructured":"Tsatsaronis G, Macari N, Torge S, et al. A Maximum-Entropy approach for accurate document annotation in the biomedical domain. J Biomed Semantics. 2012;3(Suppl 1):S2.","journal-title":"J Biomed Semantics"},{"key":"556_CR19","first-page":"1188","volume-title":"Proceedings of the 31th International Conference on Machine Learning (ICML)","author":"QV Le","year":"2014","unstructured":"Le QV, Mikolov T. Distributed Representations of Sentences and Documents. In: Proceedings of the 31th International Conference on Machine Learning (ICML), vol. 14; 2014. p. 1188\u201396."},{"key":"556_CR20","first-page":"3111","volume":"26","author":"T Mikolov","year":"2013","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado G, Dean J. Distributed representations of words and phrases and their compositionality. Adv Neural Inf Process Syst. 2013;26:3111\u20139.","journal-title":"Adv Neural Inf Process Syst"},{"issue":"8","key":"556_CR21","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell. 2013;35(8):1798\u2013828.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"556_CR22","first-page":"246","volume":"235","author":"M Hughes","year":"2017","unstructured":"Hughes M, Li I, Kotoulas S, Suzumura T. Medical text classification using convolutional neural networks. Stud Health Technol Inform. 2017;235:246\u201350.","journal-title":"Stud Health Technol Inform."},{"key":"556_CR23","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;25:1097\u2013105.","journal-title":"Adv Neural Inf Process Syst"},{"key":"556_CR24","first-page":"1610.04989","volume":"arXiv","author":"J Xu","year":"2016","unstructured":"Xu J, Chen D, Qiu X, Huang X. Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification. arXiv preprint. 2016;arXiv:1610.04989.","journal-title":"arXiv preprint"},{"key":"556_CR25","doi-asserted-by":"crossref","unstructured":"Tang D, Qin B, Liu T. Learning Semantic Representations of Users and Products for Document Level Sentiment Classification. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015;1014\u20131023.","DOI":"10.3115\/v1\/P15-1098"},{"issue":"8","key":"556_CR26","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735\u201380.","journal-title":"Neural Comput"},{"key":"556_CR27","first-page":"1509.01626","volume":"arXiv","author":"X Zhang","year":"2015","unstructured":"Zhang X, Zhao J, LeCun Y. Character-level Convolutional Networks for Text Classification. arXiv preprint. 2015;arXiv:1509.01626.","journal-title":"arXiv preprint"},{"key":"556_CR28","volume-title":"Proceedings of the 7th international workshop on Data and text mining in biomedical informatics - DTMBIO\u201913","author":"K Doing-Harris","year":"2013","unstructured":"Doing-Harris K, Patterson O, Igo S, et al. Document sublanguage clustering to detect medical specialty in cross-institutional clinical texts. In: Proceedings of the 7th international workshop on Data and text mining in biomedical informatics - DTMBIO\u201913; 2013."},{"key":"556_CR29","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198242246.001.0001","volume-title":"A theory of language and information: a mathematical approach","author":"ZS Harris","year":"1991","unstructured":"Harris ZS. A theory of language and information: a mathematical approach. Oxford and New York: Clarendon Press; 1991."},{"key":"556_CR30","unstructured":"Murphy SN, Chueh HCA. Security architecture for query tools used to access large biomedical databases. Proc AMIA Symp. 2002;2002:552\u20136."},{"key":"556_CR31","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/1472-6947-8-32","volume":"8","author":"I Neamatullah","year":"2008","unstructured":"Neamatullah I, Douglass MM, Lehman LW, et al. Automated de-identification of free-text medical records. BMC Med Inform Decis Mak. 2008;8:32.","journal-title":"BMC Med Inform Decis Mak"},{"issue":"23","key":"556_CR32","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","volume":"101","author":"AL Goldberger","year":"2000","unstructured":"Goldberger AL, Amaral LAN, Glass L, et al. PhysioBank, PhysioToolkit, and Physionet: components of a new research resource for complex physiologic signals. Circulation. 2000;101(23):e215\u201320.","journal-title":"Circulation"},{"key":"556_CR33","unstructured":"Yetisgen-Yildiz M, Pratt W. The effect of feature representation on MEDLINE document classification. AMIA Annu Symp Proc. 2005;2005:849\u201353."},{"issue":"5","key":"556_CR34","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1136\/jamia.2009.001560","volume":"17","author":"GK Savova","year":"2010","unstructured":"Savova GK, Masanz JJ, Ogren PV, et al. Mayo clinical text analysis and knowledge extraction system (cTAKES): architecture, component evaluation and applications. J Am Med Informatics Assoc. 2010;17(5):507\u201313.","journal-title":"J Am Med Informatics Assoc"},{"issue":"90001","key":"556_CR35","doi-asserted-by":"crossref","first-page":"D267","DOI":"10.1093\/nar\/gkh061","volume":"32","author":"O Bodenreider","year":"2004","unstructured":"Bodenreider O. The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 2004;32(90001):D267\u201370.","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"556_CR36","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1002\/cfg.255","volume":"4","author":"AT McCray","year":"2003","unstructured":"McCray AT. An upper-level ontology for the biomedical domain. Comp Funct Genomics. 2003;4(1):80\u20134.","journal-title":"Comp Funct Genomics"},{"issue":"Pt 1","key":"556_CR37","first-page":"216","volume":"84","author":"AT McCray","year":"2001","unstructured":"McCray AT, Burgun A, Bodenreider O, Aggregating UMLS. Semantic types for reducing conceptual complexity. Stud Health Technol Inform. 2001;84(Pt 1):216\u201320.","journal-title":"Stud Health Technol Inform"},{"issue":"5","key":"556_CR38","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/0306-4573(88)90021-0","volume":"24","author":"G Salton","year":"1988","unstructured":"Salton G, Buckley C. Term-weighting approaches in automatic text retrieval. Information Processing & Management. 1988;24(5):513\u201323.","journal-title":"Information Processing & Management"},{"issue":"3","key":"556_CR39","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1108\/eb046814","volume":"14","author":"MF Porter","year":"1980","unstructured":"Porter MF. An algorithm for suffix stripping. Program. 1980 Mar;14(3):130\u20137.","journal-title":"Program"},{"key":"556_CR40","doi-asserted-by":"crossref","first-page":"1746","DOI":"10.3115\/v1\/D14-1181","volume-title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","author":"Y Kim","year":"2014","unstructured":"Kim Y. Convolutional Neural Networks for Sentence Classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP); 2014. p. 1746\u201351."},{"key":"556_CR41","first-page":"1607.04606","volume":"arXiv","author":"P Bojanowski","year":"2016","unstructured":"Bojanowski P, Grave E, Joulin A, Mikolov T. Enriching Word Vectors with Subword Information. arXiv preprint. 2016;arXiv:1607.04606.","journal-title":"arXiv preprint"},{"key":"556_CR42","first-page":"1607.01759","volume":"arXiv","author":"A Joulin","year":"2016","unstructured":"Joulin A, Grave E, Bojanowski P, Mikolov T. Bag of Tricks for Efficient Text Classification. arXiv preprint. 2016;arXiv:1607.01759.","journal-title":"arXiv preprint"},{"issue":"3","key":"556_CR43","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273\u201397.","journal-title":"Mach Learn"},{"key":"556_CR44","first-page":"1871","volume":"9","author":"RE Fan","year":"2008","unstructured":"Fan RE, Chang KW, Wang XR, et al. LIBLINEAR: a library for large linear classification. J Mach Learn Res. 2008;9:1871\u20134.","journal-title":"J Mach Learn Res"},{"key":"556_CR45","first-page":"1507.05717","volume":"arXiv","author":"B Shi","year":"2015","unstructured":"Shi B, Bai X, Yao C. An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition. arXiv preprint. 2015;arXiv:1507.05717.","journal-title":"arXiv preprint"},{"key":"556_CR46","first-page":"1412.6980","volume":"arXiv","author":"DP Kingma","year":"2014","unstructured":"Kingma DP, Ba J. Adam: A Method for Stochastic Optimization. arXiv preprint. 2014;arXiv:1412.6980.","journal-title":"arXiv preprint"},{"key":"556_CR47","doi-asserted-by":"crossref","unstructured":"Brodersen KH, Ong CS, Stephan KE, et al. The balanced accuracy and its posterior distribution. Proceedings of the 20th international conference on pattern recognition. IEEE computer. Society. 2010:3121\u20134.","DOI":"10.1109\/ICPR.2010.764"},{"key":"556_CR48","unstructured":"Project code repository: https:\/\/github.com\/ckbjimmy\/cdc\/"},{"key":"556_CR49","first-page":"1099","volume":"2011","author":"O Patterson","year":"2011","unstructured":"Patterson O, Hurdle JF. Document clustering of clinical narratives: a systematic study of clinical sublanguages. AMIA Annu Symp Proc. 2011;2011:1099\u2013107.","journal-title":"AMIA Annu Symp Proc"},{"issue":"4\u20135","key":"556_CR50","first-page":"540","volume":"37","author":"MA Musen","year":"1998","unstructured":"Musen MA. Domain ontologies in software engineering: use of Prot\u00e9g\u00e9 with the EON architecture. Methods Inf Med. 1998;37(4\u20135):540\u201350.","journal-title":"Methods Inf Med"},{"key":"556_CR51","unstructured":"Aronson AR. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proc AMIA Symp. 2001;2001:17\u201321."},{"key":"556_CR52","unstructured":"Boag W, Wacome K, Naumann T, et al. CliNER: a lightweight tool for clinical named entity recognition [abstract]. AMIA Joint Summits on Clinical Research Informatics. 2015;"},{"key":"556_CR53","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1093\/jamia\/ocw156","volume":"24","author":"F Dernoncourt","year":"2016","unstructured":"Dernoncourt F, Lee JY, Uzuner O, et al. De-identification of patient notes with recurrent neural networks. J Am Med Inform Assoc. 2016;24:596\u2013606. doi.org\/10.1093\/jamia\/ocw156","journal-title":"J Am Med Inform Assoc"},{"issue":"7","key":"556_CR54","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1046\/j.1525-1497.2000.06269.x","volume":"15","author":"SN Weingart","year":"2000","unstructured":"Weingart SN, Ship AN, Aronson MD. Confidential clinician-reported surveillance of adverse events among medical inpatients. J Gen Intern Med. 2000;15(7):470\u20137.","journal-title":"J Gen Intern Med"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-017-0556-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,29]],"date-time":"2024-06-29T08:48:11Z","timestamp":1719650891000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-017-0556-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12]]},"references-count":54,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,12]]}},"alternative-id":["556"],"URL":"https:\/\/doi.org\/10.1186\/s12911-017-0556-8","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,12]]},"article-number":"155"}}