{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T13:11:30Z","timestamp":1771333890659,"version":"3.50.1"},"reference-count":37,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100004325","name":"AstraZeneca","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004325","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Medical Informatics"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.ijmedinf.2026.106303","type":"journal-article","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T11:36:33Z","timestamp":1769081793000},"page":"106303","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Reproducing real-world clinical prediction models using the DIVE platform: A comparative validation study across three chronic diseases"],"prefix":"10.1016","volume":"210","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4342-9128","authenticated-orcid":false,"given":"Francesco","family":"Lapi","sequence":"first","affiliation":[]},{"given":"Ettore","family":"Marconi","sequence":"additional","affiliation":[]},{"given":"Marco","family":"Gorini","sequence":"additional","affiliation":[]},{"given":"Lorenzo","family":"Nuti","sequence":"additional","affiliation":[]},{"given":"Gerardo","family":"Medea","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3961-6614","authenticated-orcid":false,"given":"Iacopo","family":"Cricelli","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.ijmedinf.2026.106303_b0005","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1002\/cpt.2449","article-title":"Use of RWE to inform regulatory, public health policy, and intervention priorities for the developing world","volume":"111","author":"McNair","year":"2022","journal-title":"Clin. Pharmacol. Ther."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0010","article-title":"STaRT-RWE: structured template for planning and reporting on the implementation of real world evidence studies","volume":"372","author":"Wang","year":"2021","journal-title":"The BMJ"},{"key":"10.1016\/j.ijmedinf.2026.106303_b0015","doi-asserted-by":"crossref","unstructured":"N. Martini, G. Trifir\u00f2, A. Capuano, G. Corrao, G. Corrao, G. Racagni, L. Pani, Expert opinion on Real World Evidence RWE in drug development and usage, Pharmadvances 02 (2020). 10.36118\/PHARMADVANCES.02.2020.01.","DOI":"10.36118\/pharmadvances.01.2020.01"},{"key":"10.1016\/j.ijmedinf.2026.106303_b0020","doi-asserted-by":"crossref","unstructured":"O. Tahri, M. Usman, C. Demonceaux, D. Fofi, M. Hittawe, Fast earth mover\u2019s distance computation for catadioptric image sequences, Proceedings - International Conference on Image Processing, ICIP 2016-August (2016) 2485\u20132489. 10.1109\/ICIP.2016.7532806.","DOI":"10.1109\/ICIP.2016.7532806"},{"key":"10.1016\/j.ijmedinf.2026.106303_b0025","doi-asserted-by":"crossref","unstructured":"O. Beya, M. Hittawe, D. Sidibe, F. Meriaudeau, Automatic Detection and Tracking of Animal Sperm Cells in Microscopy Images, Proceedings - 11th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2015 (2016) 155\u2013159. 10.1109\/SITIS.2015.111.","DOI":"10.1109\/SITIS.2015.111"},{"key":"10.1016\/j.ijmedinf.2026.106303_b0030","doi-asserted-by":"crossref","unstructured":"M. Luo, F. Xiao, Z. yu Chen, X. kang Wang, W. hui Hou, J. qiang Wang, A hybrid FSRF model based on regression algorithm for diabetes medical expense prediction, Technol Forecast Soc Change 207 (2024) 123634. 10.1016\/J.TECHFORE.2024.123634.","DOI":"10.1016\/j.techfore.2024.123634"},{"key":"10.1016\/j.ijmedinf.2026.106303_b0035","doi-asserted-by":"crossref","DOI":"10.1109\/JBHI.2025.3595140","article-title":"Improving clinical foundation models with multi-modal learning and domain adaptation for chronic disease prediction","author":"Hou","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0040","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1097\/EDE.0b013e3181c30fb2","article-title":"Assessing the performance of prediction models: a framework for traditional and novel measures","volume":"21","author":"Steyerberg","year":"2010","journal-title":"Epidemiology"},{"key":"10.1016\/j.ijmedinf.2026.106303_b0045","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1373\/clinchem.2008.115345","article-title":"Dealing with missing predictor values when applying clinical prediction models","volume":"55","author":"Janssen","year":"2009","journal-title":"Clin. Chem."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0050","doi-asserted-by":"crossref","first-page":"55","DOI":"10.7326\/M14-0697","article-title":"Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement","volume":"162","author":"Collins","year":"2015","journal-title":"Ann. Intern. Med."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmedinf.2021.104510","article-title":"The need to separate the wheat from the chaff in medical informatics: introducing a comprehensive checklist for the (self)-assessment of medical AI studies","volume":"153","author":"Cabitza","year":"2021","journal-title":"Int. J. Med. Inform."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0060","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1007\/978-1-4419-9863-7_1197","article-title":"Generalized Additive Models","author":"Higdon","year":"2013","journal-title":"Encyclopedia Syst. Biol."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0065","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jclinepi.2019.02.004","article-title":"A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models","volume":"110","author":"Christodoulou","year":"2019","journal-title":"J. Clin. Epidemiol."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0070","unstructured":"J.D.-T.J. of M. learning research, undefined 2006, Statistical comparisons of classifiers over multiple data sets, Jmlr.Org 7 (2006) 1\u201330. https:\/\/www.jmlr.org\/papers\/volume7\/demsar06a\/demsar06a.pdf (accessed December 23, 2022)."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0075","doi-asserted-by":"crossref","unstructured":"E.J. Topol, Medical forecasting, Science 384 (2024) eadp7977. 10.1126\/SCIENCE.ADP7977.","DOI":"10.1126\/science.adp7977"},{"key":"10.1016\/j.ijmedinf.2026.106303_b0080","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1186\/s12859-025-06079-3","article-title":"PyPropel: a Python-based tool for efficiently processing and characterising protein data","volume":"26","author":"Sun","year":"2025","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0085","article-title":"PyBioPortal: a Python package for simplifying cBioPortal data access in cancer research","volume":"8","author":"Valerio","year":"2025","journal-title":"JAMIA Open"},{"key":"10.1016\/j.ijmedinf.2026.106303_b0090","doi-asserted-by":"crossref","first-page":"553","DOI":"10.2307\/23042796","article-title":"Predictive analytics in information systems research","volume":"35","author":"Shmueli","year":"2011","journal-title":"MIS Q."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0095","doi-asserted-by":"crossref","first-page":"e79","DOI":"10.1017\/cts.2025.55","article-title":"EHRchitect: an open-source software tool for medical event sequences data extraction from Electronic Health Records","volume":"9","author":"Botnar","year":"2025","journal-title":"J. Clin. Transl. Sci."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0100","doi-asserted-by":"crossref","first-page":"1494","DOI":"10.1093\/jamia\/ocad097","article-title":"To predict the risk of chronic kidney disease (CKD) using Generalized Additive2 Models (GA2M)","volume":"30","author":"Lapi","year":"2023","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.rmed.2024.107634","article-title":"Development and validation of a prediction score to assess the risk of incurring in COPD-related exacerbations: a population-based study in primary care","volume":"227","author":"Lapi","year":"2024","journal-title":"Respir. Med."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0110","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1080\/03007995.2024.2341869","article-title":"Development and validation of a score assessing the risk of severe asthma in primary care","volume":"40","author":"Lapi","year":"2024","journal-title":"Curr. Med. Res. Opin."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0115","first-page":"1","article-title":"Predicting the risk of severe COVID-19 outcomes in primary care: development and validation of a vulnerability index for equitable allocation of effective vaccines","author":"Lapi","year":"2021","journal-title":"Expert Rev. Vaccines"},{"key":"10.1016\/j.ijmedinf.2026.106303_b0120","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.jclinepi.2014.11.010","article-title":"Transparent reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement","volume":"68","author":"Collins","year":"2015","journal-title":"J. Clin. Epidemiol."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0125","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.jclinepi.2015.04.005","article-title":"Prediction models need appropriate internal, internal-external, and external validation","volume":"69","author":"Steyerberg","year":"2016","journal-title":"J. Clin. Epidemiol."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0130","doi-asserted-by":"crossref","unstructured":"R. P, A. DG, External validation of a Cox prognostic model: principles and methods, BMC Med Res Methodol 13 (2013). 10.1186\/1471-2288-13-33.","DOI":"10.1186\/1471-2288-13-33"},{"key":"10.1016\/j.ijmedinf.2026.106303_b0135","doi-asserted-by":"crossref","DOI":"10.1093\/eurheartj\/ehu207","article-title":"Towards better clinical prediction models: Seven steps for development and an ABCD for validation","volume":"35","author":"Steyerberg","year":"2014","journal-title":"Eur. Heart J."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0140","unstructured":"J. Pineau, P. Vincent-Lamarre, K. Sinha, V. Larivi\u00e9re, A. Beygelzimer, F. d\u2019Alch\u00e9-Buc, E. Fox, H. Larochelle, Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program), Journal of Machine Learning Research 22 (2020) 1\u201320. https:\/\/arxiv.org\/pdf\/2003.12206 (accessed January 2, 2026)."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0145","unstructured":"Practical Machine Learning for Data Analysis Using Python - Google Books, (n.d.). https:\/\/www.google.it\/books\/edition\/Practical_Machine_Learning_for_Data_Anal\/43bLDwAAQBAJ?hl=it&gbpv=0 (accessed May 26, 2025)."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0150","unstructured":"From an expert statistician\u2019s tool kit: R vs Python programming language - IQVIA, (n.d.). https:\/\/www.iqvia.com\/blogs\/2021\/06\/from-an-expert-statisticians-tool-kit-r-vs-python-programming-language (accessed May 26, 2025)."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0155","unstructured":"Abdulhamit. Subasi, Practical machine learning for data analysis using python, (2020) 520."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0160","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s10654-020-00677-6","article-title":"Data extraction for epidemiological research (DExtER): a novel tool for automated clinical epidemiology studies","volume":"36","author":"Gokhale","year":"2021","journal-title":"Eur. J. Epidemiol."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0165","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1002\/cpt.3021","article-title":"SURF: a screening tool (for sponsors) to evaluate whether using real-world data to support an effectiveness claim in an FDA application has regulatory feasibility","volume":"114","author":"Campbell","year":"2023","journal-title":"Clin. Pharmacol. Ther."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.imu.2023.101291","article-title":"Curator \u2013 a data curation tool for clinical real-world evidence","volume":"40","author":"Delmestri","year":"2023","journal-title":"Inform Med Unlocked"},{"key":"10.1016\/j.ijmedinf.2026.106303_b0175","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1200\/CCI.19.00047","article-title":"Systematic review of privacy-preserving distributed machine learning from federated databases in health care","author":"Zerka","year":"2020","journal-title":"JCO Clin. Cancer Inform."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0180","doi-asserted-by":"crossref","first-page":"1244","DOI":"10.1093\/jamia\/ocaa096","article-title":"Fold-stratified cross-validation for unbiased and privacy-preserving federated learning","volume":"27","author":"Bey","year":"2020","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.ijmedinf.2026.106303_b0185","unstructured":"Regulation - 2016\/679 - EN - gdpr - EUR-Lex, (n.d.). https:\/\/eur-lex.europa.eu\/eli\/reg\/2016\/679\/oj (accessed May 31, 2024)."}],"container-title":["International Journal of Medical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1386505626000432?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1386505626000432?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T12:20:25Z","timestamp":1771330825000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1386505626000432"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":37,"alternative-id":["S1386505626000432"],"URL":"https:\/\/doi.org\/10.1016\/j.ijmedinf.2026.106303","relation":{},"ISSN":["1386-5056"],"issn-type":[{"value":"1386-5056","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Reproducing real-world clinical prediction models using the DIVE platform: A comparative validation study across three chronic diseases","name":"articletitle","label":"Article Title"},{"value":"International Journal of Medical Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ijmedinf.2026.106303","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"106303"}}