{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,9,23]],"date-time":"2023-09-23T10:23:06Z","timestamp":1695464586412},"reference-count":113,"publisher":"Springer Science and Business Media LLC","issue":"S1","license":[{"start":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T00:00:00Z","timestamp":1572566400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2019,11,12]],"date-time":"2019-11-12T00:00:00Z","timestamp":1573516800000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Biomed Semant"],"published-print":{"date-parts":[[2019,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Deep Learning opens up opportunities for routinely scanning large bodies of biomedical literature and clinical narratives to represent the meaning of biomedical and clinical terms. However, the validation and integration of this knowledge on a scale requires cross checking with ground truths (i.e. evidence-based resources) that are unavailable in an actionable or computable form. In this paper we explore how to turn information about diagnoses, prognoses, therapies and other clinical concepts into computable knowledge using free-text data about human and animal health. We used a Semantic Deep Learning approach that combines the Semantic Web technologies and Deep Learning to acquire and validate knowledge about 11 well-known medical conditions mined from two sets of unstructured free-text data: 300\u2009K PubMed Systematic Review articles (the PMSB dataset) and 2.5\u2009M veterinary clinical notes (the VetCN dataset). For each target condition we obtained 20 related clinical concepts using two deep learning methods applied separately on the two datasets, resulting in 880 term pairs (target term, candidate term). Each concept, represented by an n-gram, is mapped to UMLS using MetaMap; we also developed a bespoke method for mapping short forms (e.g. abbreviations and acronyms). Existing ontologies were used to formally represent associations. We also create ontological modules and illustrate how the extracted knowledge can be queried. The evaluation was performed using the content within BMJ Best Practice.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>MetaMap achieves an F measure of 88% (precision 85%, recall 91%) when applied directly to the total of 613 unique candidate terms for the 880 term pairs. When the processing of short forms is included, MetaMap achieves an F measure of 94% (precision 92%, recall 96%). Validation of the term pairs with BMJ Best Practice yields precision between 98 and 99%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The Semantic Deep Learning approach can transform neural embeddings built from unstructured free-text data into reliable and reusable One Health knowledge using ontologies and content from BMJ Best Practice.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s13326-019-0212-6","type":"journal-article","created":{"date-parts":[[2019,11,12]],"date-time":"2019-11-12T01:02:36Z","timestamp":1573520556000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Exploring semantic deep learning for building reliable and reusable one health knowledge from PubMed systematic reviews and veterinary clinical notes"],"prefix":"10.1186","volume":"10","author":[{"given":"Mercedes","family":"Arguello-Casteleiro","sequence":"first","affiliation":[]},{"given":"Robert","family":"Stevens","sequence":"additional","affiliation":[]},{"given":"Julio","family":"Des-Diz","sequence":"additional","affiliation":[]},{"given":"Chris","family":"Wroe","sequence":"additional","affiliation":[]},{"given":"Maria Jesus","family":"Fernandez-Prieto","sequence":"additional","affiliation":[]},{"given":"Nava","family":"Maroto","sequence":"additional","affiliation":[]},{"given":"Diego","family":"Maseda-Fernandez","sequence":"additional","affiliation":[]},{"given":"George","family":"Demetriou","sequence":"additional","affiliation":[]},{"given":"Simon","family":"Peters","sequence":"additional","affiliation":[]},{"given":"Peter-John M.","family":"Noble","sequence":"additional","affiliation":[]},{"given":"Phil H.","family":"Jones","sequence":"additional","affiliation":[]},{"given":"Jo","family":"Dukes-McEwan","sequence":"additional","affiliation":[]},{"given":"Alan D.","family":"Radford","sequence":"additional","affiliation":[]},{"given":"John","family":"Keane","sequence":"additional","affiliation":[]},{"given":"Goran","family":"Nenadic","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,11,12]]},"reference":[{"key":"212_CR1","unstructured":"WHO: One Health. September 2017. http:\/\/www.who.int\/features\/qa\/one-health\/en\/."},{"issue":"7647","key":"212_CR2","doi-asserted-by":"crossref","first-page":"S47","DOI":"10.1038\/543S47a","volume":"543","author":"LH Kahn","year":"2017","unstructured":"Kahn LH. Perspective: the one-health way. Nature. 2017;543(7647):S47.","journal-title":"Nature"},{"key":"212_CR3","doi-asserted-by":"crossref","unstructured":"Stroud, C., Dmitriev, I., Kashentseva, E., Bryan, J.N., Curiel, D.T., Rindt, H., Reinero, C., Henry, C.J., Bergman, P.J., Mason, N.J. and Gnanandarajah, J.S., 2016, August. A One Health overview, facilitating advances in comparative medicine and translational research. In Clinical and translational medicine (Vol. 5, No. 1, p. 26). Springer Berlin Heidelberg.","DOI":"10.1186\/s40169-016-0107-4"},{"key":"212_CR4","unstructured":"Semantic Deep Learning. http:\/\/semdeep.iiia.csic.es."},{"key":"212_CR5","unstructured":"Semantic Deep Learning. http:\/\/www.semantic-web-journal.net\/blog\/call-papers-special-issue-semantic-deep-learning. Accessed 25th April 2019."},{"issue":"7553","key":"212_CR6","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436\u201344.","journal-title":"Nature"},{"issue":"7023","key":"212_CR7","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1136\/bmj.312.7023.71","volume":"312","author":"DL Sackett","year":"1996","unstructured":"Sackett DL, Rosenberg W, Gray JA, Haynes RB, Richardson WS. Evidence based medicine: what it is and what it isn't. Bmj. 1996;312(7023):71\u20132.","journal-title":"Bmj"},{"key":"212_CR8","unstructured":"BMJ Best Practice. https:\/\/bestpractice.bmj.com."},{"key":"212_CR9","unstructured":"DynaMed Plus. http:\/\/www.dynamed.com\/."},{"key":"212_CR10","unstructured":"UpToDate. https:\/\/www.uptodate.com\/."},{"key":"212_CR11","unstructured":"The National Institute for Health and Care Excellence (NICE). https:\/\/www.nice.org.uk\/."},{"key":"212_CR12","doi-asserted-by":"crossref","DOI":"10.17226\/1626","volume-title":"Committee to advise the public health service on clinical practice guidelines IoM: clinical practice guidelines: directions for a new program","author":"MJ Field","year":"1990","unstructured":"Field MJ, Lohr KN. Committee to advise the public health service on clinical practice guidelines IoM: clinical practice guidelines: directions for a new program. Washington, D.C.: National Academy Press; 1990."},{"issue":"12","key":"212_CR13","first-page":"829","volume":"13","author":"D Rebholz-Schuhmann","year":"2012","unstructured":"Rebholz-Schuhmann D, Oellrich A, Hoehndorf R. Text-mining solutions for biomedical research: enabling integrative biology. Nature reviews. Genetics. 2012;13(12):829\u201339.","journal-title":"Genetics"},{"key":"212_CR14","unstructured":"UMLS. https:\/\/www.nlm.nih.gov\/research\/umls\/index.html."},{"key":"212_CR15","unstructured":"MeSH. https:\/\/www.nlm.nih.gov\/mesh\/."},{"issue":"5","key":"212_CR16","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1136\/amiajnl-2010-000055","volume":"18","author":"M Huang","year":"2011","unstructured":"Huang M, N\u00e9v\u00e9ol A, Lu Z. Recommending MeSH terms for annotating biomedical articles. J Am Med Inform Assoc. 2011;18(5):660\u20137.","journal-title":"J Am Med Inform Assoc"},{"key":"212_CR17","unstructured":"PubMed Systematic Reviews, https:\/\/www.nlm.nih.gov\/bsd\/pubmed_subsets\/sysreviews_strategy.html."},{"key":"212_CR18","unstructured":"One Health Initiative. http:\/\/www.onehealthinitiative.com."},{"key":"212_CR19","unstructured":"SAVSNET. https:\/\/www.liverpool.ac.uk\/savsnet\/. Accessed 25th April 2019."},{"key":"212_CR20","first-page":"245","volume-title":"Lecture Notes in Computer Science","author":"John McCrae","year":"2011","unstructured":"McCrae, J., Spohr, D. and Cimiano, P., 2011, May. Linking lexical resources and ontologies on the semantic web with lemon. In extended semantic web conference (pp. 245-259). Springer, Berlin, Heidelberg."},{"issue":"1","key":"212_CR21","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s13326-016-0051-7","volume":"7","author":"S Sarntivijai","year":"2016","unstructured":"Sarntivijai S, Vasant D, Jupp S, Saunders G, Bento AP, Gonzalez D, Betts J, Hasan S, Koscielny G, Dunham I, Parkinson H, Malone J. Linking rare and common disease: mapping clinical disease-phenotypes to ontologies in therapeutic target validation. J Biomed Semantics. 2016;7(1):8.","journal-title":"J Biomed Semantics"},{"issue":"1","key":"212_CR22","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1631\/FITEE.1700808","volume":"19","author":"Q-S Zhang","year":"2018","unstructured":"Zhang Q-S, Zhu S-C. Visual interpretability for deep learning: a survey. Frontiers of Information Technology and Electronic Engineering. 2018;19(1):27\u201339.","journal-title":"Frontiers of Information Technology and Electronic Engineering"},{"issue":"2","key":"212_CR23","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.jbi.2004.02.001","volume":"37","author":"JE Caviedes","year":"2004","unstructured":"Caviedes JE, Cimino JJ. Towards the development of a conceptual distance metric for the UMLS. J Biomed Inform. 2004;37(2):77\u201385.","journal-title":"J Biomed Inform"},{"issue":"3","key":"212_CR24","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.jbi.2006.06.004","volume":"40","author":"T Pedersen","year":"2007","unstructured":"Pedersen T, Pakhomov SV, Patwardhan S, Chute CG. Measures of semantic similarity and relatedness in the biomedical domain. J Biomed Inform. 2007;40(3):288\u201399.","journal-title":"J Biomed Inform"},{"issue":"2","key":"212_CR25","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.jbi.2010.10.004","volume":"44","author":"SV Pakhomov","year":"2011","unstructured":"Pakhomov SV, Pedersen T, McInnes B, Melton GB, Ruggieri A, Chute CG. Towards a framework for developing semantic relatedness reference standards. J Biomed Inform. 2011;44(2):251\u201365.","journal-title":"J Biomed Inform"},{"key":"212_CR26","unstructured":"Pakhomov, S., McInnes, B., Adam, T., Liu, Y., Pedersen, T. and Melton, G.B., 2010. Semantic similarity and relatedness between clinical terms: an experimental study. In AMIA annual symposium proceedings (Vol. 2010, p. 572). American medical informatics association."},{"key":"212_CR27","unstructured":"Semantic similarity and relatedness resources. http:\/\/rxinformatics.umn.edu\/SemanticRelatednessResources.html."},{"issue":"2","key":"212_CR28","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.jbi.2009.02.002","volume":"42","author":"T Cohen","year":"2009","unstructured":"Cohen T, Widdows D. Empirical distributional semantics: methods and biomedical applications. J Biomed Inform. 2009;42(2):390\u2013405.","journal-title":"J Biomed Inform"},{"issue":"2","key":"212_CR29","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1037\/0033-295X.104.2.211","volume":"104","author":"TK Landauer","year":"1997","unstructured":"Landauer TK, Dumais ST. A solution to Plato's problem: the latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychol Rev. 1997;104(2):211.","journal-title":"Psychol Rev"},{"issue":"Jan","key":"212_CR30","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. J Mach Learn Res. 2003;3(Jan):993\u20131022.","journal-title":"J Mach Learn Res"},{"key":"212_CR31","doi-asserted-by":"crossref","unstructured":"Faruqui, M., Tsvetkov, Y., Rastogi, P. and Dyer, C., 2016. Problems with evaluation of word Embeddings using word similarity tasks. In proceedings of the 1st workshop on evaluating vector-space representations for NLP (pp. 30-35).","DOI":"10.18653\/v1\/W16-2506"},{"issue":"4","key":"212_CR32","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1162\/COLI_a_00237","volume":"41","author":"F Hill","year":"2015","unstructured":"Hill F, Reichart R, Korhonen A. Simlex-999: evaluating semantic models with (genuine) similarity estimation. Computational Linguistics. 2015;41(4):665\u201395.","journal-title":"Computational Linguistics"},{"key":"212_CR33","doi-asserted-by":"crossref","unstructured":"Gerz D, Vuli\u0107 I, Hill F, Reichart R, Korhonen A. SimVerb-3500: a large-scale evaluation set of verb similarity. EMNLP 2016. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing; 2016, pp. 2173\u2013182.","DOI":"10.18653\/v1\/D16-1235"},{"issue":"suppl_1","key":"212_CR34","first-page":"D289","volume":"33","author":"JD Wren","year":"2005","unstructured":"Wren JD, Chang JT, Pustejovsky J, Adar E, Garner HR, Altman RB. Biomedical term mapping databases. Nucleic Acids Res. 2005;33(suppl_1):D289\u201393.","journal-title":"Nucleic Acids Res"},{"key":"212_CR35","unstructured":"Liu, H., Lussier, Y.A. and Friedman, C., 2001. A study of abbreviations in the UMLS. In proceedings of the AMIA symposium (p. 393-7). American medical informatics association."},{"key":"212_CR36","unstructured":"Xu, H., Stetson, P.D. and Friedman, C., 2007. A study of abbreviations in clinical notes. In AMIA annual symposium proceedings (Vol. 2007, p. 821-5). American medical informatics association."},{"issue":"2","key":"212_CR37","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1136\/amiajnl-2012-001506","volume":"21","author":"S Moon","year":"2013","unstructured":"Moon S, Pakhomov S, Liu N, Ryan JO, Melton GB. A sense inventory for clinical abbreviations and acronyms created using clinical notes and medical dictionary resources. J Am Med Inform Assoc. 2013;21(2):299\u2013307.","journal-title":"J Am Med Inform Assoc"},{"issue":"4","key":"212_CR38","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1093\/bioinformatics\/btg439","volume":"20","author":"E Adar","year":"2004","unstructured":"Adar E. SaRAD: a simple and robust abbreviation dictionary. Bioinformatics. 2004;20(4):527\u201333.","journal-title":"Bioinformatics"},{"issue":"22","key":"212_CR39","doi-asserted-by":"crossref","first-page":"2813","DOI":"10.1093\/bioinformatics\/btl480","volume":"22","author":"W Zhou","year":"2006","unstructured":"Zhou W, Torvik VI, Smalheiser NR. ADAM: another database of abbreviations in MEDLINE. Bioinformatics. 2006;22(22):2813\u20138.","journal-title":"Bioinformatics"},{"key":"212_CR40","doi-asserted-by":"crossref","unstructured":"Yamamoto Y, Yamaguchi A, Bono H, Takagi T. Allie: a database and a search service of abbreviations and long forms. Database. 2011;2011.","DOI":"10.1093\/database\/bar013"},{"key":"212_CR41","unstructured":"Xu, H., Stetson, P.D. and Friedman, C., 2007. A study of abbreviations in clinical notes. In AMIA annual symposium proceedings (Vol. 2007, p. 821-825). American medical informatics association."},{"issue":"1","key":"212_CR42","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1197\/jamia.M2927","volume":"16","author":"H Xu","year":"2009","unstructured":"Xu H, Stetson PD, Friedman C. Methods for building sense inventories of abbreviations in clinical notes. J Am Med Inform Assoc. 2009;16(1):103\u20138.","journal-title":"J Am Med Inform Assoc"},{"key":"212_CR43","unstructured":"Wu, Y., Denny, J.C., Rosenbloom, S.T., Miller, R.A., Giuse, D.A. and Xu, H., 2012. A comparative study of current clinical natural language processing systems on handling abbreviations in discharge summaries. In AMIA annual symposium proceedings (Vol. 2012, p. 997-1003). American medical informatics association."},{"key":"212_CR44","unstructured":"Arguello Casteleiro M., G. Demetriou, W.J. Read, M.J. Fernandez-Prieto, D. Maseda-Fernandez, G. Nenadic, J. Klein, J.A. Keane, R. Stevens. (Sept 2016). Deep Learning meets Semantic Web: A feasibility study with the Cardiovascular Disease Ontology and PubMed citations. In Proceedings of the 7th Workshop on Ontologies and Data in Life Sciences, organized by the GI Workgroup Ontologies in Biomedicine and Life Sciences (OBML). CEUR Vol. 1692."},{"key":"212_CR45","unstructured":"Arguello Casteleiro M., M., Prieto, M.J.F., Demetriou, G., Maroto, N., Read, W.J., Maseda-Fernandez, D., Des Diz, J.J., Nenadic, G., Keane, J.A. and Stevens, R., 2016. Ontology Learning with Deep Learning: a Case Study on Patient Safety Using PubMed In SWAT4LS."},{"key":"212_CR46","unstructured":"Arguello Casteleiro M., D. Maseda-Fernandez, G. Demetriou, W. Read, M.J. Fernandez-Prieto, J. Des-Diz, G. Nenadic, J. Keane, and R. Stevens (April 2017). A case study on Sepsis using PubMed and Deep Learning for Ontology Learning. In Proceedings of Informatics for Health 2017. In \"Studies in Health Technology and Informatics\" by IOS Press."},{"key":"212_CR47","unstructured":"Arguello Casteleiro M., C. Mart\u00ednez Costa, J. Des-Diz, M.J. Fernandez-Prieto, C. Wroe, D. Maseda-Fernandez, G. Demetriou, G. Nenadic, J. Keane, S. Schulz and R. Stevens (Dec 2017). Experiments to create ontology-based disease models for diabetic retinopathy from different biomedical resources. In proceedings of semantic web applications and tools for health care and life sciences (SWAT4HCLS 2017). CEUR Vol."},{"key":"212_CR48","unstructured":"SPARQL query language. https:\/\/www.w3.org\/TR\/sparql11-query\/."},{"key":"212_CR49","unstructured":"Apache Jena ARQ. https:\/\/jena.apache.org\/documentation\/query\/index.html."},{"key":"212_CR50","unstructured":"VetSCT. https:\/\/www.nlm.nih.gov\/research\/umls\/sourcereleasedocs\/current\/SNOMEDCT_VET\/."},{"key":"212_CR51","unstructured":"UMLS API. https:\/\/documentation.uts.nlm.nih.gov."},{"issue":"1","key":"212_CR52","doi-asserted-by":"crossref","first-page":"11","DOI":"10.3233\/SW-2011-0025","volume":"2","author":"M Horridge","year":"2011","unstructured":"Horridge M, Bechhofer S. The owl api: a java api for owl ontologies. Semantic Web. 2011;2(1):11\u201321.","journal-title":"Semantic Web"},{"key":"212_CR53","unstructured":"word2vec. http:\/\/code.google.com\/p\/word2vec\/."},{"key":"212_CR54","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. Distributed representations of words and phrases and their compositionality. InAdvances in neural information processing systems 2013; 3111\u20133119."},{"key":"212_CR55","doi-asserted-by":"crossref","unstructured":"Arguello-Casteleiro, M., Demetriou, G., Read, W., Prieto, M.J.F., Maroto, N., Fernandez, D.M., Nenadic, G., Klein, J., Keane, J. and Stevens, R., 2018. Deep learning meets ontologies: experiments to anchor the cardiovascular disease ontology in the biomedical literature . J Biomed Semantics, 9(1), p.13","DOI":"10.1186\/s13326-018-0181-1"},{"key":"212_CR56","first-page":"12","volume":"2016","author":"MT Pilehvar","year":"2016","unstructured":"Pilehvar MT, Collier N. Improved semantic representation for domain-specific entities. ACL. 2016;2016:12.","journal-title":"ACL"},{"key":"212_CR57","unstructured":"Pyysalo, S., Ginter, F., Moen, H., Salakoski, T., & Ananiadou, S.: Distributional semantics resources for biomedical text pro-cessing. In Proc. of Languages in Biology and Medicine (2013)."},{"key":"212_CR58","unstructured":"Muneeb TH, Sahu SK, Anand A. Evaluating distributed word representations for capturing semantics of biomedical concepts: Proceedings of ACL-IJCNLP; 2015. p. 158."},{"key":"212_CR59","unstructured":"Minarro-Gim\u00e9nez, J. A., Mar\u00edn-Alonso, O., & Samwald, M.: Exploring the application of deep learning techniques on medical text corpora. In e-Health \u2013 for continuity of care, IOS Press, pp. 584\u2013588 (2014)."},{"issue":"23","key":"212_CR60","doi-asserted-by":"crossref","first-page":"3635","DOI":"10.1093\/bioinformatics\/btw529","volume":"32","author":"SV Pakhomov","year":"2016","unstructured":"Pakhomov SV, Finley G, McEwan R, Wang Y, Melton GB. Corpus domain effects on distributional semantic modeling of medical terms. Bioinformatics. 2016;32(23):3635\u201344.","journal-title":"Bioinformatics"},{"issue":"5","key":"212_CR61","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1136\/amiajnl-2011-000464","volume":"18","author":"PM Nadkarni","year":"2011","unstructured":"Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. J Am Med Inform Assoc. 2011;18(5):544\u201351.","journal-title":"J Am Med Inform Assoc"},{"key":"212_CR62","unstructured":"SNOMED CT Compositional Grammar v2.3.1. http:\/\/snomed.org\/scg."},{"key":"212_CR63","unstructured":"SI unit. https:\/\/bitbucket.org\/birkenfeld\/ipython-physics\/raw\/default\/physics.py ."},{"key":"212_CR64","unstructured":"Other non-SI units. https:\/\/www.bipm.org\/utils\/common\/pdf\/si_brochure_8_en.pdf."},{"key":"212_CR65","unstructured":"Units of Length, Mass, and Liquid Volume. https:\/\/www.nist.gov\/sites\/default\/files\/documents\/2017\/04\/28\/AppC-12-hb44-final.pdf. Accessed 25th April 2019."},{"key":"212_CR66","unstructured":"Word Frequencies in Written and Spoken English: based on the British National Corpus. http:\/\/ucrel.lancs.ac.uk\/bncfreq\/flists.html."},{"key":"212_CR67","unstructured":"Manning CD, Sch\u00fctze H. Foundations of statistical natural language processing: MIT press; 1999."},{"key":"212_CR68","unstructured":"Pratt, W. and Yetisgen-Yildiz, M., 2003. A study of biomedical concept identification: MetaMap vs. people. In AMIA annual symposium proceedings (Vol. 2003, p. 529-533). American medical informatics association."},{"key":"212_CR69","doi-asserted-by":"crossref","unstructured":"Smucker, M.D., Allan, J. and Carterette, B., 2007. A comparison of statistical significance tests for information retrieval evaluation. In proceedings of the sixteenth ACM conference on conference on information and knowledge management (pp. 623-632). ACM.","DOI":"10.1145\/1321440.1321528"},{"key":"212_CR70","unstructured":"Box GE, Hunter WG, Hunter JS. Statistics for experimenters: John Wiley & Sons; 1978."},{"issue":"Oct","key":"212_CR71","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12(Oct):2825\u201330.","journal-title":"J Mach Learn Res"},{"issue":"7","key":"212_CR72","doi-asserted-by":"crossref","first-page":"1714","DOI":"10.1097\/CCM.0000000000000325","volume":"42","author":"SM Opal","year":"2014","unstructured":"Opal SM, Dellinger RP, Vincent JL, Masur H, Angus DC. The next generation of sepsis trials: What\u2019s next after the demise of recombinant human activated protein C ? critical care medicine. 2014;42(7):1714.","journal-title":"critical care medicine"},{"key":"212_CR73","unstructured":"Extended lemon core ontology. http:\/\/semdeep.cs.man.ac.uk\/inOWL\/lemonEXT_core.owl."},{"key":"212_CR74","unstructured":"Modified OBAN core ontology. http:\/\/semdeep.cs.man.ac.uk\/inOWL\/OBANmod_core.owl."},{"key":"212_CR75","unstructured":"UMLS Semantic Types and Groups. https:\/\/metamap.nlm.nih.gov\/Docs\/SemGroups_2013.txt."},{"key":"212_CR76","unstructured":"Basic Formal Ontology (BFO). http:\/\/www.obofoundry.org\/ontology\/bfo.html."},{"key":"212_CR77","unstructured":"oboInOwl meta-model. http:\/\/www.geneontology.org\/formats\/oboInOwl."},{"key":"212_CR78","unstructured":"Horridge, M., Drummond, N., Goodwin, J., Rector, A.L., Stevens, R. and Wang, H., 2006. The Manchester OWL syntax. In OWLed (Vol. 216)."},{"key":"212_CR79","unstructured":"Relations Ontology (RO). http:\/\/www.ontobee.org\/ontology\/RO."},{"key":"212_CR80","unstructured":"Ontology Lexicon (Ontolex). https:\/\/www.w3.org\/2016\/05\/ontolex\/."},{"key":"212_CR81","first-page":"43","volume-title":"New Trends of Research in Ontologies and Lexical Resources","author":"Philipp Cimiano","year":"2012","unstructured":"Cimiano, P., McCrae, J., Buitelaar, P. and Montiel-Ponsoda, E., 2013. On the role of senses in the ontology-lexicon. In new trends of research in ontologies and lexical resources (pp. 43-62). Springer, Berlin, Heidelberg."},{"key":"212_CR82","unstructured":"UMLS Semantic Types. https:\/\/www.nlm.nih.gov\/research\/umls\/META3_current_semantic_types.html ."},{"key":"212_CR83","unstructured":"Evidence & Conclusion Ontology (ECO). http:\/\/purl.obolibrary.org\/obo\/eco.owl ."},{"key":"212_CR84","unstructured":"Bibliographic Ontology Specification ontology (BIBO). http:\/\/purl.org\/ontology\/bibo\/ ."},{"key":"212_CR85","unstructured":"BMJ Best Practice: Chronic congestive heart failure. http:\/\/bestpractice.bmj.com\/topics\/en-gb\/61."},{"key":"212_CR86","unstructured":"MedlinePlus. https:\/\/medlineplus.gov\/ ."},{"key":"212_CR87","doi-asserted-by":"crossref","unstructured":"Grau, B.C., Horrocks, I., Kazakov, Y. and Sattler, U., 2007, May. Just the right amount: extracting modules from ontologies. In proceedings of the 16th international conference on world wide web (pp. 717-726). ACM.","DOI":"10.1145\/1242572.1242669"},{"key":"212_CR88","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1007\/11814771_26","volume-title":"Automated Reasoning","author":"Dmitry Tsarkov","year":"2006","unstructured":"Tsarkov, D. and Horrocks, I., 2006, August. FaCT++ description logic reasoner: system description. In international joint conference on automated reasoning (pp. 292-297). Springer, Berlin, Heidelberg."},{"key":"212_CR89","unstructured":"Spelling Corrector. http:\/\/norvig.com\/spell-correct.html."},{"issue":"4","key":"212_CR90","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1177\/0883073807302758","volume":"22","author":"R Korinthenberg","year":"2007","unstructured":"Korinthenberg R, Burkart P, Woelfle C, Moenting JS, Ernst JP. Pharmacology, efficacy, and tolerability of potassium bromide in childhood epilepsy. J Child Neurol. 2007;22(4):414\u20138.","journal-title":"J Child Neurol"},{"issue":"1","key":"212_CR91","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s40263-013-0129-z","volume":"28","author":"C Rundfeldt","year":"2014","unstructured":"Rundfeldt C, L\u00f6scher W. The pharmacology of imepitoin: the first partial benzodiazepine receptor agonist developed for the treatment of epilepsy. CNS drugs. 2014;28(1):29\u201343.","journal-title":"CNS drugs"},{"key":"212_CR92","unstructured":"BMJ Best Practice: Open-angle glaucoma. http:\/\/bestpractice.bmj.com\/topics\/en-gb\/373."},{"issue":"11","key":"212_CR93","first-page":"8163","volume":"7","author":"M Zhao","year":"2014","unstructured":"Zhao M, Mu Y, Dang Y, Zhu Y. Secondary glaucoma as initial manifestation of ring melanoma: a case report and review of literature. Int J Clin Exp Pathol. 2014;7(11):8163.","journal-title":"Int J Clin Exp Pathol"},{"key":"212_CR94","unstructured":"BMJ Best Practice: Obesity in adults. http:\/\/bestpractice.bmj.com\/topics\/en-gb\/211."},{"issue":"17","key":"212_CR95","doi-asserted-by":"crossref","first-page":"2723","DOI":"10.1093\/bioinformatics\/btx275","volume":"33","author":"M Alshahrani","year":"2017","unstructured":"Alshahrani M, Khan MA, Maddouri O, Kinjo AR, Queralt-Rosinach N, Hoehndorf R. Neuro-symbolic representation learning on biological knowledge graphs. Bioinformatics. 2017;33(17):2723\u201330.","journal-title":"Bioinformatics"},{"key":"212_CR96","doi-asserted-by":"crossref","unstructured":"Jauhar, S.K., Dyer, C. and Hovy, E., 2015. Ontologically grounded multi-sense representation learning for semantic vector space models. In proceedings of the 2015 conference of the north American chapter of the Association for Computational Linguistics: human language technologies (pp. 683-693).","DOI":"10.3115\/v1\/N15-1070"},{"issue":"3","key":"212_CR97","doi-asserted-by":"crossref","first-page":"e0193094","DOI":"10.1371\/journal.pone.0193094","volume":"13","author":"M Alsuhaibani","year":"2018","unstructured":"Alsuhaibani M, Bollegala D, Maehara T, Kawarabayashi KI. Jointly learning word embeddings using a corpus and a knowledge base. PloS One. 2018;13(3):e0193094.","journal-title":"PloS One"},{"key":"212_CR98","unstructured":"Turian, J., Ratinov, L. and Bengio, Y., 2010. Word representations: a simple and general method for semi-supervised learning. In proceedings of the 48th annual meeting of the association for computational linguistics (pp. 384-394). Association for Computational Linguistics."},{"key":"212_CR99","unstructured":"Huang, E.H., Socher, R., Manning, C.D. and Ng, A.Y., 2012. Improving word representations via global context and multiple word prototypes. In proceedings of the 50th annual meeting of the Association for Computational Linguistics: long papers-volume 1 (pp. 873-882). Association for Computational Linguistics."},{"issue":"15","key":"212_CR100","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1001\/jama.280.15.1347","volume":"280","author":"WR Hersh","year":"1998","unstructured":"Hersh WR, Hickam DH. How well do physicians use electronic information retrieval systems?: a framework for investigation and systematic review. Jama. 1998;280(15):1347\u201352.","journal-title":"Jama"},{"issue":"12","key":"212_CR101","doi-asserted-by":"crossref","first-page":"2119","DOI":"10.1007\/s00134-003-1942-5","volume":"29","author":"GS Doig","year":"2003","unstructured":"Doig GS, Simpson F. Efficient literature searching: a core skill for the practice of evidence-based medicine. Intensive Care Med. 2003;29(12):2119\u201327.","journal-title":"Intensive Care Med"},{"key":"212_CR102","unstructured":"Cochrane Handbook for Systematic Reviews of Interventions. http:\/\/handbook-5-1.cochrane.org."},{"key":"212_CR103","unstructured":"UMLS 2018AA. https:\/\/www.nlm.nih.gov\/pubs\/techbull\/mj18\/mj18_umls_2018aa_release.html."},{"key":"212_CR104","unstructured":"McInnes, B.T., Pedersen, T. and Pakhomov, S.V., 2009. UMLS-Interface and UMLS-similarity: open source software for measuring paths and semantic similarity. In AMIA annual symposium proceedings (Vol. 2009, p. 431). American medical informatics association."},{"key":"212_CR105","unstructured":"UMLS-Similarity Web Interface. http:\/\/maraca.d.umn.edu\/cgi-bin\/umls_similarity\/umls_similarity.cgi."},{"key":"212_CR106","unstructured":"UMLS-Similarity: Relatedness measures. http:\/\/maraca.d.umn.edu\/umls_similarity\/relatedness_measures.html."},{"key":"212_CR107","unstructured":"BMJ Best Practice: Asthma in adults. http:\/\/bestpractice.bmj.com\/topics\/en-gb\/44."},{"key":"212_CR108","unstructured":"BMJ Best Practice: Asthma in children. http:\/\/bestpractice.bmj.com\/topics\/en-gb\/782."},{"issue":"3","key":"212_CR109","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1136\/jamia.2009.002733","volume":"17","author":"AR Aronson","year":"2010","unstructured":"Aronson AR, Lang FM. An overview of MetaMap: historical perspective and recent advances. J Am Med Inform Assoc. 2010;17(3):229\u201336.","journal-title":"J Am Med Inform Assoc"},{"key":"212_CR110","unstructured":"Personalised Health and Care 2020: A Framework for Action. https:\/\/www.gov.uk\/government\/publications\/personalised-health-and-care-2020."},{"issue":"1","key":"212_CR111","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.jbi.2012.09.006","volume":"46","author":"D Lee","year":"2013","unstructured":"Lee D, Cornet R, Lau F, De Keizer N. A survey of SNOMED CT implementations. J Biomed Inform. 2013;46(1):87\u201396.","journal-title":"J Biomed Inform"},{"key":"212_CR112","unstructured":"SNOMED CT\u00ae Technical Implementation Guide. January 2015 International Release. https:\/\/confluence.ihtsdotools.org\/display\/DOCTIG\/Technical+Implementation+Guide."},{"key":"212_CR113","unstructured":"NHS: SNOMED CT human-readable subsets. https:\/\/isd.digital.nhs.uk\/trud3\/user\/guest\/group\/0\/pack\/40."}],"container-title":["Journal of Biomedical Semantics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13326-019-0212-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13326-019-0212-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13326-019-0212-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T20:41:39Z","timestamp":1695415299000},"score":1,"resource":{"primary":{"URL":"https:\/\/jbiomedsem.biomedcentral.com\/articles\/10.1186\/s13326-019-0212-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11]]},"references-count":113,"journal-issue":{"issue":"S1","published-print":{"date-parts":[[2019,11]]}},"alternative-id":["212"],"URL":"https:\/\/doi.org\/10.1186\/s13326-019-0212-6","relation":{},"ISSN":["2041-1480"],"issn-type":[{"value":"2041-1480","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11]]},"assertion":[{"value":"12 November 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"22"}}