{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T12:52:28Z","timestamp":1767703948141,"version":"3.37.3"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100004489","name":"Mitacs","doi-asserted-by":"publisher","award":["IT27834"],"award-info":[{"award-number":["IT27834"]}],"id":[{"id":"10.13039\/501100004489","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000142","name":"Arthritis Society","doi-asserted-by":"crossref","award":["20-000000018","20-000000018","20-000000018","20-000000018","20-000000018","20-000000018","20-000000018"],"award-info":[{"award-number":["20-000000018","20-000000018","20-000000018","20-000000018","20-000000018","20-000000018","20-000000018"]}],"id":[{"id":"10.13039\/501100000142","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"DOI":"10.1186\/s12911-024-02776-w","type":"journal-article","created":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T10:32:42Z","timestamp":1732703562000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Development and validation of a rheumatoid arthritis case definition: a machine learning approach using data from primary care electronic medical records"],"prefix":"10.1186","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8967-513X","authenticated-orcid":false,"given":"Anh N. Q.","family":"Pham","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3062-5488","authenticated-orcid":false,"given":"Claire E. H.","family":"Barber","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Neil","family":"Drummond","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lisa","family":"Jasper","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Doug","family":"Klein","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cliff","family":"Lindeman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7464-0460","authenticated-orcid":false,"given":"Jessica","family":"Widdifield","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tyler","family":"Williamson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C. Allyson","family":"Jones","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"issue":"7","key":"2776_CR1","doi-asserted-by":"publisher","first-page":"1316","DOI":"10.1136\/annrheumdis-2013-204627","volume":"73","author":"M Cross","year":"2014","unstructured":"Cross M, Smith E, Hoy D, Carmona L, Wolfe F, Vos T, et al. The global burden of rheumatoid arthritis: estimates from the global burden of disease 2010 study. Ann Rheum Dis. 2014;73(7):1316\u201322.","journal-title":"Ann Rheum Dis"},{"issue":"3","key":"2776_CR2","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1136\/annrheumdis-2020-218282","volume":"80","author":"BR England","year":"2021","unstructured":"England BR, Roul P, Yang Y, Sayles H, Yu F, Michaud K, et al. Burden and trajectory of multimorbidity in rheumatoid arthritis: a matched cohort study from 2006 to 2015. Ann Rheum Dis. 2021;80(3):286\u201392.","journal-title":"Ann Rheum Dis"},{"issue":"11","key":"2776_CR3","doi-asserted-by":"publisher","first-page":"2857","DOI":"10.3390\/cells10112857","volume":"10","author":"AF Radu","year":"2021","unstructured":"Radu AF, Bungau SG. Management of rheumatoid arthritis: an overview. Cells. 2021;10(11):2857.","journal-title":"Cells"},{"issue":"10","key":"2776_CR4","first-page":"1219","volume":"57","author":"RV Birtwhistle","year":"2011","unstructured":"Birtwhistle RV. Canadian Primary Care Sentinel Surveillance Network: a developing resource for family medicine and public health. Can Fam Physician Med Fam Can. 2011;57(10):1219\u201320.","journal-title":"Can Fam Physician Med Fam Can"},{"issue":"4","key":"2776_CR5","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1093\/ije\/dyw248","volume":"46","author":"S Garies","year":"2017","unstructured":"Garies S, Birtwhistle R, Drummond N, Queenan J, Williamson T. Data resource profile: national electronic medical record data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN. Int J Epidemiol. 2017;46(4):1091\u20132.","journal-title":"Int J Epidemiol."},{"key":"2776_CR6","unstructured":"CPCSSN. CPCSSN Case Definition Version 2. 2019. https:\/\/cpcssn.ca\/wp-content\/uploads\/2023\/03\/CPCSSN-Case-Definitions-2022-Q4_v2.pdf.\u00a0Cited 20 Jul 2023."},{"issue":"9","key":"2776_CR7","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1002\/sim.4780120902","volume":"12","author":"SE Vollset","year":"1993","unstructured":"Vollset SE. Confidence intervals for a binomial proportion. Stat Med. 1993;12(9):809\u201324.","journal-title":"Stat Med"},{"key":"2776_CR8","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1370\/afm.1644","volume":"12","author":"T Williamson","year":"2014","unstructured":"Williamson T, Green ME, Birtwhistle R, Khan S, Garies S, Wong ST, et al. Validating the 8 CPCSSN case definitions for chronic disease surveillance in a primary care database of electronic health records. Ann Fam Med. 2014;12:367\u201372.","journal-title":"Ann Fam Med"},{"issue":"3","key":"2776_CR9","doi-asserted-by":"publisher","first-page":"276","DOI":"10.11613\/BM.2012.031","volume":"22","author":"ML McHugh","year":"2012","unstructured":"McHugh ML. Interrater reliability: the kappa statistic. Biochem Med. 2012;22(3):276\u201382.","journal-title":"Biochem Med"},{"key":"2776_CR10","doi-asserted-by":"publisher","unstructured":"Pham ANQ, Cummings M, Yuksel N, Sydora B, Williamson T, Garies S et al. Development and Validation of a Machine Learning Algorithm for Problematic Menopause in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN. 2023. https:\/\/doi.org\/10.21203\/rs.3.rs-2403081\/v1. Cited 13 Mar 2023.","DOI":"10.21203\/rs.3.rs-2403081\/v1"},{"key":"2776_CR11","unstructured":"Hannun A, Guo C, van der Maaten L. Measuring Data Leakage in Machine-Learning Models with Fisher Information. arXiv; 2021. http:\/\/arxiv.org\/abs\/2102.11673. Cited 17 May 2023."},{"key":"2776_CR12","doi-asserted-by":"publisher","first-page":"307","DOI":"10.3389\/fpubh.2017.00307","volume":"5","author":"R Trevethan","year":"2017","unstructured":"Trevethan R, Sensitivity. Specificity, and predictive values: foundations, pliabilities, and pitfalls in Research and Practice. Front Public Health. 2017;5:307.","journal-title":"Front Public Health"},{"issue":"2","key":"2776_CR13","doi-asserted-by":"publisher","first-page":"e100453","DOI":"10.1136\/gpsych-2020-100453","volume":"34","author":"H Wang","year":"2021","unstructured":"Wang H, Wang B, Zhang X, Feng C. Relations among sensitivity, specificity and predictive values of medical tests based on biomarkers. Gen Psychiatry. 2021;34(2):e100453.","journal-title":"Gen Psychiatry"},{"key":"2776_CR14","doi-asserted-by":"publisher","first-page":"670670","DOI":"10.3389\/fphar.2021.670670","volume":"12","author":"S Seo","year":"2021","unstructured":"Seo S, Kim Y, Han HJ, Son WC, Hong ZY, Sohn I, et al. Predicting successes and failures of clinical trials with outer product\u2013based convolutional neural network. Front Pharmacol. 2021;12:670670.","journal-title":"Front Pharmacol"},{"issue":"1","key":"2776_CR15","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s12864-019-6413-7","volume":"21","author":"D Chicco","year":"2020","unstructured":"Chicco D, Jurman G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics. 2020;21(1):6.","journal-title":"BMC Genomics"},{"key":"2776_CR16","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res"},{"key":"2776_CR17","doi-asserted-by":"publisher","first-page":"901428","DOI":"10.3389\/fninf.2022.901428","volume":"16","author":"S Suresh","year":"2022","unstructured":"Suresh S, Newton DT, Everett TH, Lin G, Duerstock BS. Feature selection techniques for a machine learning model to detect autonomic Dysreflexia. Front Neuroinformatics. 2022;16:901428.","journal-title":"Front Neuroinformatics"},{"key":"2776_CR18","first-page":"69","volume":"79","author":"Leordeanu C Truic\u0103CO","year":"2017","unstructured":"Truic\u0103CO Leordeanu C. Classication of an Imbalanced Data Set using Decision TreeAlgorithms. Univ Politeh Buchar Sci Bull Ser C - Electr Eng Comput Sci. 2017;79:69.","journal-title":"Univ Politeh Buchar Sci Bull Ser C - Electr Eng Comput Sci."},{"issue":"1","key":"2776_CR19","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45(1):5\u201332.","journal-title":"Mach Learn"},{"key":"2776_CR20","doi-asserted-by":"publisher","unstructured":"Chen T, Guestrin C, XGBoost:. A Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining [Internet]. New York, NY, USA: Association for Computing Machinery; 2016. pp. 785\u201394. (KDD \u201916). Available from:\u00a0https:\/\/doi.org\/10.1145\/2939672.2939785.\u00a0Cited 19 Sep 2023.","DOI":"10.1145\/2939672.2939785"},{"issue":"4","key":"2776_CR21","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1370\/afm.1644","volume":"12","author":"T Williamson","year":"2014","unstructured":"Williamson T, Green ME, Birtwhistle R, Khan S, Garies S, Wong ST, et al. Validating the 8 CPCSSN case definitions for chronic disease surveillance in a primary care database of electronic health records. Ann Fam Med. 2014;12(4):367\u201372.","journal-title":"Ann Fam Med"},{"key":"2776_CR22","unstructured":"Canadian Chronic Disease Surveillance System (CCDSS). https:\/\/health-infobase.canada.ca\/ccdss\/data-tool\/. Cited 24 Apr 2023."},{"issue":"4","key":"2776_CR23","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1002\/art.38306","volume":"66","author":"J Widdifield","year":"2014","unstructured":"Widdifield J, Paterson JM, Bernatsky S, Tu K, Tomlinson G, Kuriya B, et al. The epidemiology of rheumatoid arthritis in Ontario, Canada. Arthritis Rheumatol Hoboken NJ. 2014;66(4):786\u201393.","journal-title":"Arthritis Rheumatol Hoboken NJ"},{"issue":"12","key":"2776_CR24","doi-asserted-by":"publisher","first-page":"e009309","DOI":"10.1136\/bmjopen-2015-009309","volume":"5","author":"S Muller","year":"2015","unstructured":"Muller S, Hider SL, Raza K, Stack RJ, Hayward RA, Mallen CD. An algorithm to identify rheumatoid arthritis in primary care: a clinical Practice Research Datalink study. BMJ Open. 2015;5(12):e009309.","journal-title":"BMJ Open"},{"issue":"5","key":"2776_CR25","doi-asserted-by":"publisher","first-page":"e0154515","DOI":"10.1371\/journal.pone.0154515","volume":"11","author":"SM Zhou","year":"2016","unstructured":"Zhou SM, Fernandez-Gutierrez F, Kennedy J, Cooksey R, Atkinson M, Denaxas S, et al. Defining Disease phenotypes in Primary Care Electronic Health Records by a machine Learning Approach: a case study in identifying rheumatoid arthritis. PLoS ONE. 2016;11(5):e0154515.","journal-title":"PLoS ONE"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02776-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-024-02776-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02776-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T11:03:33Z","timestamp":1732705413000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-024-02776-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,27]]},"references-count":25,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["2776"],"URL":"https:\/\/doi.org\/10.1186\/s12911-024-02776-w","relation":{},"ISSN":["1472-6947"],"issn-type":[{"type":"electronic","value":"1472-6947"}],"subject":[],"published":{"date-parts":[[2024,11,27]]},"assertion":[{"value":"4 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study has received approval from the Health Research Ethics Board at the University of Alberta (Pro00107346) and the University of Calgary (REB21-0723) and adheres to all relevant guidelines and regulations for research involving de-identified health data (e.g. CHREB, Tri-Council Policy Statement on the Ethical Conduct for Research Involving Humans [TCPS2]).The CPCSSN database has received ethics approval, including waivers of <i>individual patient informed consent<\/i> for their de-identified data to be used for surveillance and research, from each contributing network\u2019s local Research Ethics Board (Queen\u2019s University, Memorial University of Newfoundland, University of Ottawa, University of Calgary, Dalhouse University, University of Toronto, Western University, Bruy\u00e8re Research Institute, University of British Columbia, University of Alberta, and University of Manitoba).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"360"}}