{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T04:24:32Z","timestamp":1786940672436,"version":"build-2736575974"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Rheumatol Int"],"DOI":"10.1007\/s00296-026-06219-9","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T02:45:37Z","timestamp":1783997137000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Tree-based machine learning models recover rule-based flare labels from virtual symptom diaries in pediatric Beh\u00e7et\u2019s disease: an in silico proof-of-concept study"],"prefix":"10.1007","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5588-0162","authenticated-orcid":false,"given":"Muhammed Talha","family":"Karado\u011fan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0702-6313","authenticated-orcid":false,"given":"Turan","family":"G\u00fczel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9394-0610","authenticated-orcid":false,"given":"Bu\u015fra","family":"\u00c7inpolat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1667-2480","authenticated-orcid":false,"given":"Figen","family":"\u00c7akmak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"issue":"4","key":"6219_CR1","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1016\/j.jaad.2007.10.452","volume":"58","author":"Y Karincaoglu","year":"2008","unstructured":"Karincaoglu Y, Borlu M, Toker SC, Akman A, Onder M, Gunasti S et al (2008) Demographic and clinical properties of juvenile-onset Beh\u00e7et\u2019s disease: a controlled multicenter study. J Am Acad Dermatol 58(4):579\u2013584. https:\/\/doi.org\/10.1016\/j.jaad.2007.10.452","journal-title":"J Am Acad Dermatol"},{"issue":"6","key":"6219_CR2","doi-asserted-by":"publisher","first-page":"958","DOI":"10.1136\/annrheumdis-2015-208491","volume":"75","author":"I Kon\u00e9-Paut","year":"2016","unstructured":"Kon\u00e9-Paut I, Shahram F, Darce-Bello M, Cantarini L, Cimaz R, Gattorno M et al (2016) Consensus classification criteria for paediatric Beh\u00e7et\u2019s disease from a prospective observational cohort: PEDBD. Ann Rheum Dis 75(6):958\u2013964. https:\/\/doi.org\/10.1136\/annrheumdis-2015-208491","journal-title":"Ann Rheum Dis"},{"issue":"1","key":"6219_CR3","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s12969-016-0070-z","volume":"14","author":"I Kon\u00e9-Paut","year":"2016","unstructured":"Kon\u00e9-Paut I (2016) Beh\u00e7et\u2019s disease in children, an overview. Pediatr Rheumatol Online J 14(1):10. https:\/\/doi.org\/10.1186\/s12969-016-0070-z","journal-title":"Pediatr Rheumatol Online J"},{"issue":"6","key":"6219_CR4","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1111\/j.1365-4632.2006.02754.x","volume":"45","author":"M Borlu","year":"2006","unstructured":"Borlu M, Uk\u015fal U, Ferahba\u015f A, Evereklioglu C (2006) Clinical features of Beh\u00e7et\u2019s disease in children. Int J Dermatol 45(6):713\u2013716. https:\/\/doi.org\/10.1111\/j.1365-4632.2006.02754.x","journal-title":"Int J Dermatol"},{"issue":"5 Suppl 101","key":"6219_CR5","first-page":"S11","volume":"34","author":"A Consolaro","year":"2016","unstructured":"Consolaro A, Morgan EM, Giancane G, Rosina S, Lanni S, Ravelli A (2016) Information technology in paediatric rheumatology. Clin Exp Rheumatol 34(5 Suppl 101):S11\u2013S16 PMID: 27762188","journal-title":"Clin Exp Rheumatol"},{"key":"6219_CR6","doi-asserted-by":"publisher","first-page":"e63639","DOI":"10.2196\/63639","volume":"27","author":"M Sasseville","year":"2025","unstructured":"Sasseville M, Supper W, Gartner JB, Layani G, Borduas C, Goyer R et al (2025) Electronic implementation of patient-reported outcome measures in primary health care: mixed methods systematic review. J Med Internet Res 27:e63639. https:\/\/doi.org\/10.2196\/63639","journal-title":"J Med Internet Res"},{"issue":"4","key":"6219_CR7","doi-asserted-by":"publisher","first-page":"62","DOI":"10.3390\/biotech12040062","volume":"12","author":"ER Jupe","year":"2023","unstructured":"Jupe ER, Lushington GH, Purushothaman M, Pautasso F, Armstrong G, Sorathia A et al (2023) Tracking of systemic lupus erythematosus (SLE) longitudinally using biosensor and patient-reported data: a report on the fully decentralized mobile study to measure and predict lupus disease activity using digital signals\u2014the OASIS study. BioTech (Basel) 12(4):62. https:\/\/doi.org\/10.3390\/biotech12040062","journal-title":"BioTech (Basel)"},{"key":"6219_CR8","doi-asserted-by":"publisher","first-page":"e50679","DOI":"10.2196\/50679","volume":"8","author":"J Gandrup","year":"2024","unstructured":"Gandrup J, Selby DA, Dixon WG (2024) Classifying self-reported rheumatoid arthritis flares using daily patient-generated data from a smartphone app: exploratory analysis applying machine learning approaches. JMIR Form Res 8:e50679. https:\/\/doi.org\/10.2196\/50679","journal-title":"JMIR Form Res"},{"issue":"20","key":"6219_CR9","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","volume":"132","author":"RC Deo","year":"2015","unstructured":"Deo RC (2015) Machine learning in medicine. Circulation 132(20):1920\u20131930. https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.115.001593","journal-title":"Circulation"},{"issue":"13","key":"6219_CR10","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1001\/jama.2017.18391","volume":"319","author":"AL Beam","year":"2018","unstructured":"Beam AL, Kohane IS (2018) Big data and machine learning in health care. JAMA 319(13):1317\u20131318. https:\/\/doi.org\/10.1001\/jama.2017.18391","journal-title":"JAMA"},{"issue":"11","key":"6219_CR11","doi-asserted-by":"publisher","first-page":"2315","DOI":"10.1007\/s00296-024-05661-x","volume":"44","author":"O Koker","year":"2024","unstructured":"Koker O, Sahin S, Yildiz M, Adrovic A, Kasapcopur O (2024) The emerging paradigm in pediatric rheumatology: harnessing the power of artificial intelligence. Rheumatol Int 44(11):2315\u20132325. https:\/\/doi.org\/10.1007\/s00296-024-05661-x","journal-title":"Rheumatol Int"},{"issue":"1","key":"6219_CR12","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1186\/s12911-023-02130-6","volume":"23","author":"N Hammam","year":"2023","unstructured":"Hammam N, Bakhiet A, El-Latif EA, El-Gazzar II, Samy N, Noor RAA et al (2023) Development of machine learning models for detection of vision-threatening Beh\u00e7et\u2019s disease (BD) using Egyptian College of Rheumatology (ECR)-BD cohort. BMC Med Inf Decis Mak 23(1):37. https:\/\/doi.org\/10.1186\/s12911-023-02130-6","journal-title":"BMC Med Inf Decis Mak"},{"issue":"1","key":"6219_CR13","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1038\/s41746-020-00353-9","volume":"3","author":"A Tucker","year":"2020","unstructured":"Tucker A, Wang Z, Rotalinti Y, Myles P (2020) Generating high-fidelity synthetic patient data for assessing machine learning healthcare software. NPJ Digit Med 3(1):147. https:\/\/doi.org\/10.1038\/s41746-020-00353-9","journal-title":"NPJ Digit Med"},{"issue":"1","key":"6219_CR14","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1186\/s12874-020-00977-1","volume":"20","author":"A Goncalves","year":"2020","unstructured":"Goncalves A, Ray P, Soper B, Stevens J, Coyle L, Sales AP (2020) Generation and evaluation of synthetic patient data. BMC Med Res Methodol 20(1):108. https:\/\/doi.org\/10.1186\/s12874-020-00977-1","journal-title":"BMC Med Res Methodol"},{"key":"6219_CR15","doi-asserted-by":"publisher","first-page":"2892","DOI":"10.1016\/j.csbj.2024.07.005","volume":"23","author":"VC Pezoulas","year":"2024","unstructured":"Pezoulas VC, Zaridis DI, Mylona E, Androutsos C, Apostolidis K, Tachos NS et al (2024) Synthetic data generation methods in healthcare: a review on open-source tools and methods. Comput Struct Biotechnol J 23:2892\u20132910. https:\/\/doi.org\/10.1016\/j.csbj.2024.07.005","journal-title":"Comput Struct Biotechnol J"},{"key":"6219_CR16","doi-asserted-by":"publisher","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","volume":"385","author":"GS Collins","year":"2024","unstructured":"Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B et al (2024) TRIPOD\u2009+\u2009AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 385:e078378. https:\/\/doi.org\/10.1136\/bmj-2023-078378","journal-title":"BMJ"},{"issue":"6","key":"6219_CR17","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1136\/bmjebm-2025-113825","volume":"30","author":"X Luo","year":"2025","unstructured":"Luo X, Tham YC, Giuffr\u00e8 M, Ranisch R, Daher M, Lam K et al (2025) GAMER Working Group. Reporting guideline for the use of Generative Artificial intelligence tools in MEdical Research: the GAMER Statement. BMJ Evid Based Med 30(6):390\u2013400. https:\/\/doi.org\/10.1136\/bmjebm-2025-113825","journal-title":"BMJ Evid Based Med"},{"key":"6219_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-52452-8","volume-title":"Time series analysis and its applications: with R examples","author":"RH Shumway","year":"2017","unstructured":"Shumway RH, Stoffer DS (2017) Time series analysis and its applications: with R examples, 4th edn. Springer, Cham","edition":"4"},{"issue":"6","key":"6219_CR19","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1136\/annrheumdis-2018-213225","volume":"77","author":"G Hatemi","year":"2018","unstructured":"Hatemi G, Christensen R, Bang D, Bodaghi B, Celik AF, Fortune F et al (2018) 2018 update of the EULAR recommendations for the management of Beh\u00e7et\u2019s syndrome. Ann Rheum Dis 77(6):808\u2013818. https:\/\/doi.org\/10.1136\/annrheumdis-2018-213225","journal-title":"Ann Rheum Dis"},{"issue":"12","key":"6219_CR20","doi-asserted-by":"publisher","first-page":"2158","DOI":"10.1111\/1756-185X.13109","volume":"21","author":"A Khabbazi","year":"2018","unstructured":"Khabbazi A, Karkon Shayan F, Ghojazadeh M, Kavandi H, Hajialiloo M, Esalatmanesh K et al (2018) Adherence to treatment in patients with Beh\u00e7et\u2019s disease. Int J Rheum Dis 21(12):2158\u20132166. https:\/\/doi.org\/10.1111\/1756-185X.13109","journal-title":"Int J Rheum Dis"},{"key":"6219_CR21","doi-asserted-by":"publisher","first-page":"495","DOI":"10.2147\/TCRM.S232660","volume":"16","author":"G Costagliola","year":"2020","unstructured":"Costagliola G, Cappelli S, Consolini R (2020) Beh\u00e7et\u2019s disease in children: diagnostic and management challenges. Ther Clin Risk Manag 16:495\u2013507. https:\/\/doi.org\/10.2147\/TCRM.S232660","journal-title":"Ther Clin Risk Manag"},{"issue":"1","key":"6219_CR22","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 (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21(1):6. https:\/\/doi.org\/10.1186\/s12864-019-6413-7","journal-title":"BMC Genomics"},{"issue":"6","key":"6219_CR23","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1177\/0272989X06295361","volume":"26","author":"AJ Vickers","year":"2006","unstructured":"Vickers AJ, Elkin EB (2006) Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak 26(6):565\u2013574. https:\/\/doi.org\/10.1177\/0272989X06295361","journal-title":"Med Decis Mak"},{"key":"6219_CR24","doi-asserted-by":"publisher","first-page":"4768","DOI":"10.48550\/arXiv.1705.07874","volume":"30","author":"SM Lundberg","year":"2017","unstructured":"Lundberg SM, Lee S-I (2017) A unified approach to interpreting model predictions. Adv Neural Inf Process Syst 30:4768\u20134777. https:\/\/doi.org\/10.48550\/arXiv.1705.07874","journal-title":"Adv Neural Inf Process Syst"},{"issue":"9","key":"6219_CR25","doi-asserted-by":"publisher","first-page":"e0000299","DOI":"10.1371\/journal.pdig.0000299","volume":"3","author":"C Lokker","year":"2024","unstructured":"Lokker C, Abdelkader W, Bagheri E, Parrish R, Cotoi C, Navarro T et al (2024) Boosting efficiency in a clinical literature surveillance system with LightGBM. PLOS Digit Health 3(9):e0000299. https:\/\/doi.org\/10.1371\/journal.pdig.0000299","journal-title":"PLOS Digit Health"},{"issue":"1","key":"6219_CR26","doi-asserted-by":"publisher","first-page":"rkaa005","DOI":"10.1093\/rap\/rkaa005","volume":"4","author":"M H\u00fcgle","year":"2020","unstructured":"H\u00fcgle M, Omoumi P, van Laar JM, Boedecker J, H\u00fcgle T (2020) Applied machine learning and artificial intelligence in rheumatology. Rheumatol Adv Pract 4(1):rkaa005. https:\/\/doi.org\/10.1093\/rap\/rkaa005","journal-title":"Rheumatol Adv Pract"}],"container-title":["Rheumatology International"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00296-026-06219-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00296-026-06219-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00296-026-06219-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T03:37:28Z","timestamp":1786937848000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00296-026-06219-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,14]]},"references-count":26,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["6219"],"URL":"https:\/\/doi.org\/10.1007\/s00296-026-06219-9","relation":{},"ISSN":["1437-160X"],"issn-type":[{"value":"1437-160X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,14]]},"assertion":[{"value":"28 April 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"<b>Declarations<\/b>"}},{"value":"This study used simulated (virtual) patient data and did not involve real human participants. Accordingly, prospective institutional review board approval was not required for this work. No human or animal participants, biological samples, identifiable data, or third-party datasets were collected, processed, or stored at any stage. The study was conducted in accordance with the principles of the Declaration of Helsinki to the extent applicable to synthetic-data research.","order":2,"name":"Ethics","label":"Ethical approval and consent to participate","group":{"name":"EthicsHeading","label":"<b>Declarations<\/b>"}}],"article-number":"204"}}