{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T16:03:27Z","timestamp":1793808207758,"version":"4.6.1"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T00:00:00Z","timestamp":1762300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T00:00:00Z","timestamp":1762300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Res Methodol"],"DOI":"10.1186\/s12874-025-02689-w","type":"journal-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T11:36:07Z","timestamp":1762342567000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Assessing the accuracy of survival machine learning and traditional statistical models for Alzheimer's disease prediction over time: a study on the ADNI cohort"],"prefix":"10.1186","volume":"25","author":[{"given":"Sardar","family":"Jahani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ghodratollah","family":"Roshanaei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4378-3143","authenticated-orcid":false,"given":"Leili","family":"Tapak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,5]]},"reference":[{"issue":"1","key":"2689_CR1","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1111\/ene.13439","volume":"25","author":"CA Lane","year":"2018","unstructured":"Lane CA, Hardy J, Schott JM. Alzheimer\u2019s disease. Eur J Neurol. 2018;25(1):59\u201370.","journal-title":"Eur J Neurol"},{"issue":"3","key":"2689_CR2","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1038\/nrneurol.2011.2","volume":"7","author":"C Reitz","year":"2011","unstructured":"Reitz C, Brayne C, Mayeux R. Epidemiology of Alzheimer disease. Nat Rev Neurol. 2011;7(3):137\u201352.","journal-title":"Nat Rev Neurol"},{"issue":"2","key":"2689_CR3","doi-asserted-by":"publisher","first-page":"S3","DOI":"10.1097\/00019442-199821001-00002","volume":"6","author":"HC Hendrie","year":"1998","unstructured":"Hendrie HC. Epidemiology of dementia and Alzheimer\u2019s disease. Am J Geriatr Psychiatry. 1998;6(2):S3\u201318.","journal-title":"Am J Geriatr Psychiatry"},{"issue":"2","key":"2689_CR4","doi-asserted-by":"publisher","first-page":"e105","DOI":"10.1016\/S2468-2667(21)00249-8","volume":"7","author":"E Nichols","year":"2022","unstructured":"Nichols E, Steinmetz JD, Vollset SE, Fukutaki K, Chalek J, Abd-Allah F, et al. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health. 2022;7(2):e105\u201325.","journal-title":"Lancet Public Health"},{"issue":"1","key":"2689_CR5","doi-asserted-by":"publisher","DOI":"10.1186\/s12929-023-00976-6","volume":"30","author":"L-K Huang","year":"2023","unstructured":"Huang L-K, Kuan Y-C, Lin H-W, Hu C-J. Clinical trials of new drugs for Alzheimer disease: a 2020\u20132023 update. J Biomed Sci. 2023;30(1):83.","journal-title":"J Biomed Sci"},{"key":"2689_CR6","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.neurobiolaging.2022.10.005","volume":"121","author":"G Mirabnahrazam","year":"2023","unstructured":"Mirabnahrazam G, Ma D, Beaulac C, Lee S, Popuri K, Lee H, et al. Predicting time-to-conversion for dementia of Alzheimer\u2019s type using multi-modal deep survival analysis. Neurobiol Aging. 2023;121:139\u201356.","journal-title":"Neurobiol Aging"},{"key":"2689_CR7","doi-asserted-by":"crossref","unstructured":"Bayta\u015f \u0130M. Predicting progression from mild cognitive impairment to Alzheimer\u2019s dementia with adversarial attacks. IEEE J Biomed Health Inform. 2024;28(6):3750\u201361.","DOI":"10.1109\/JBHI.2024.3373703"},{"key":"2689_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13195-019-0501-4","volume":"11","author":"D Siedlecki-Wullich","year":"2019","unstructured":"Siedlecki-Wullich D, Catal\u00e0-Solsona J, F\u00e1bregas C, Hern\u00e1ndez I, Clarimon J, Lle\u00f3 A, et al. Altered microRNAs related to synaptic function as potential plasma biomarkers for Alzheimer\u2019s disease. Alzheimer\u2019s research & therapy. 2019;11:1\u201311.","journal-title":"Alzheimer's research & therapy"},{"key":"2689_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2021.101910","volume":"90","author":"SA Mofrad","year":"2021","unstructured":"Mofrad SA, Lundervold A, Lundervold AS, Initiative AsDN. A predictive framework based on brain volume trajectories enabling early detection of Alzheimer\u2019s disease. Comput Med Imaging Graph. 2021;90:101910.","journal-title":"Comput Med Imaging Graph"},{"issue":"4","key":"2689_CR10","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1109\/JBHI.2019.2934230","volume":"24","author":"W Li","year":"2019","unstructured":"Li W, Zhang L, Qiao L, Shen D. Toward a better estimation of functional brain network for mild cognitive impairment identification: a transfer learning view. IEEE J Biomed Health Inform. 2019;24(4):1160\u20138.","journal-title":"IEEE J Biomed Health Inform"},{"issue":"5","key":"2689_CR11","doi-asserted-by":"publisher","DOI":"10.3390\/info15050249","volume":"15","author":"G Grammenos","year":"2024","unstructured":"Grammenos G, Vrahatis AG, Vlamos P, Palejev D, Exarchos T, Initiative AsDN. Predicting the conversion from mild cognitive impairment to Alzheimer\u2019s disease using an explainable AI approach. Information. 2024;15(5):249.","journal-title":"Information"},{"issue":"22","key":"2689_CR12","doi-asserted-by":"publisher","DOI":"10.3390\/cells13221920","volume":"13","author":"M-K Park","year":"2024","unstructured":"Park M-K, Ahn J, Lim J-M, Han M, Lee J-W, Lee J-C, et al. A transcriptomics-based machine learning model discriminating mild cognitive impairment and the prediction of conversion to Alzheimer\u2019s disease. Cells. 2024;13(22):1920.","journal-title":"Cells"},{"issue":"3","key":"2689_CR13","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.3390\/curroncol28030149","volume":"28","author":"A Mitsala","year":"2021","unstructured":"Mitsala A, Tsalikidis C, Pitiakoudis M, Simopoulos C, Tsaroucha AK. Artificial intelligence in colorectal cancer screening, diagnosis and treatment. A new era. Curr Oncol. 2021;28(3):1581\u2013607.","journal-title":"Curr Oncol"},{"key":"2689_CR14","doi-asserted-by":"publisher","unstructured":"Collett D. Modelling survival data in medical research. 4th ed. Chapman and Hall\/CRC; 2023. https:\/\/doi.org\/10.1201\/9781003282525.","DOI":"10.1201\/9781003282525"},{"issue":"6","key":"2689_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3214306","volume":"51","author":"P Wang","year":"2019","unstructured":"Wang P, Li Y, Reddy CK. Machine learning for survival analysis: a survey. ACM Comput Surv. 2019;51(6):1\u201336.","journal-title":"ACM Comput Surv"},{"issue":"1","key":"2689_CR16","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1002\/sim.4780140108","volume":"14","author":"D Faraggi","year":"1995","unstructured":"Faraggi D, Simon R. A neural network model for survival data. Stat Med. 1995;14(1):73\u201382.","journal-title":"Stat Med"},{"key":"2689_CR17","doi-asserted-by":"crossref","unstructured":"Shivaswamy PK, Chu W, Jansche M. A support vector approach to censored targets. In: Seventh IEEE international conference on data mining (ICDM 2007). IEEE; 2007:655\u201360.","DOI":"10.1109\/ICDM.2007.93"},{"key":"2689_CR18","first-page":"1","volume":"2013","author":"Y Chen","year":"2013","unstructured":"Chen Y, Jia Z, Mercola D, Xie X. A gradient boosting algorithm for survival analysis via direct optimization of concordance index. Comput Math Methods Med. 2013;2013:1\u2013873595.","journal-title":"Comput Math Methods Med"},{"issue":"3","key":"2689_CR19","first-page":"841","volume":"2","author":"H Ishwaran","year":"2008","unstructured":"Ishwaran H, Kogalur UB, Blackstone EH, et al. Lauer MS. Random survival forests Ann Appl Stat. 2008;2(3):841\u201360.","journal-title":"Random survival forests. Ann Appl Stat."},{"issue":"9","key":"2689_CR20","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13092001","volume":"13","author":"AY Abuhelwa","year":"2021","unstructured":"Abuhelwa AY, Kichenadasse G, McKinnon RA, Rowland A, Hopkins AM, Sorich MJ. Machine learning for prediction of survival outcomes with immune-checkpoint inhibitors in urothelial cancer. Cancers (Basel). 2021;13(9):2001.","journal-title":"Cancers (Basel)"},{"key":"2689_CR21","doi-asserted-by":"publisher","DOI":"10.2196\/44417","volume":"25","author":"X Yang","year":"2023","unstructured":"Yang X, Qiu H, Wang L, Wang X. Predicting colorectal cancer survival using time-to-event machine learning: retrospective cohort study. J Med Internet Res. 2023;25:e44417.","journal-title":"J Med Internet Res"},{"issue":"14","key":"2689_CR22","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1056\/NEJMra1814259","volume":"380","author":"A Rajkomar","year":"2019","unstructured":"Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347\u201358.","journal-title":"N Engl J Med"},{"issue":"3","key":"2689_CR23","doi-asserted-by":"publisher","DOI":"10.3390\/biology12030442","volume":"12","author":"T Cavalcante","year":"2023","unstructured":"Cavalcante T, Ospina R, Leiva V, Cabezas X, Martin-Barreiro C. Weibull regression and machine learning survival models: methodology, comparison, and application to biomedical data related to cardiac surgery. Biology. 2023;12(3):442.","journal-title":"Biology"},{"issue":"1","key":"2689_CR24","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-72790-5","volume":"14","author":"AE Kolasseri","year":"2024","unstructured":"Kolasseri AE. Comparative study of machine learning and statistical survival models for enhancing cervical cancer prognosis and risk factor assessment using SEER data. Sci Rep. 2024;14(1):22203.","journal-title":"Sci Rep"},{"key":"2689_CR25","doi-asserted-by":"crossref","unstructured":"Sarica A, Aracri F, Bianco MG, Vaccaro MG, Quattrone A, Quattrone A: Conversion from mild cognitive impairment to Alzheimer\u2019s disease: A comparison of tree-based machine learning algorithms for survival analysis. In: International conference on brain informatics. Springer; 2023:179\u201390.","DOI":"10.1007\/978-3-031-43075-6_16"},{"issue":"1","key":"2689_CR26","first-page":"e12035","volume":"12","author":"CM Fiford","year":"2020","unstructured":"Fiford CM, Nicholas JM, Biessels GJ, Lane CA, Cardoso MJ, Barnes J. High blood pressure predicts hippocampal atrophy rate in cognitively impaired elders. Alzheimer\u2019s & Dementia: Diagnosis, Assessment & Disease Monitoring. 2020;12(1):e12035.","journal-title":"Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring"},{"issue":"3","key":"2689_CR27","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci14030201","volume":"14","author":"A Sarica","year":"2024","unstructured":"Sarica A, Pelagi A, Aracri F, Arcuri F, Quattrone A, Quattrone A. Sex differences in conversion risk from mild cognitive impairment to Alzheimer\u2019s disease: an explainable machine learning study with random survival forests and SHAP. Brain Sci. 2024;14(3):201.","journal-title":"Brain Sci"},{"issue":"1","key":"2689_CR28","doi-asserted-by":"publisher","first-page":"e12301","DOI":"10.1002\/trc2.12301","volume":"8","author":"M Wang","year":"2022","unstructured":"Wang M, Sajobi TT, Ismail Z, Seitz D, Chekouo T, Forkert ND, et al. A pragmatic dementia risk score for patients with mild cognitive impairment in a memory clinic population: development and validation of a dementia risk score using routinely collected data. Alzheimer\u2019s & Dementia: Translational Research & Clinical Interventions. 2022;8(1):e12301.","journal-title":"Alzheimer's & Dementia: Translational Research & Clinical Interventions"},{"issue":"10","key":"2689_CR29","doi-asserted-by":"publisher","first-page":"7320","DOI":"10.1002\/alz.14099","volume":"20","author":"AW Toga","year":"2024","unstructured":"Toga AW, Neu S, Sheehan ST, Crawford K, Initiative AsDN. The informatics of ADNI. Alzheimers Dement. 2024;20(10):7320\u201330.","journal-title":"Alzheimers Dement"},{"issue":"1","key":"2689_CR30","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1093\/bioinformatics\/btr597","volume":"28","author":"DJ Stekhoven","year":"2012","unstructured":"Stekhoven DJ, B\u00fchlmann P. Missforest\u2014non-parametric missing value imputation for mixed-type data. Bioinformatics. 2012;28(1):112\u20138.","journal-title":"Bioinformatics"},{"key":"2689_CR31","doi-asserted-by":"crossref","unstructured":"Aracri F, Bianco MG, Quattrone A, Sarica A: Imputation of missing clinical, cognitive and neuroimaging data of Dementia using missForest, a Random Forest based algorithm. In: 2023 IEEE 36th international symposium on computer-based medical systems (CBMS). IEEE; 2023:684\u20138.","DOI":"10.1109\/CBMS58004.2023.00300"},{"issue":"2","key":"2689_CR32","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","volume":"34","author":"DR Cox","year":"1972","unstructured":"Cox DR. Regression models and life-tables. Journal of the Royal Statistical Society Series B: Statistical Methodology. 1972;34(2):187\u2013202.","journal-title":"Journal of the Royal Statistical Society Series B: Statistical Methodology"},{"key":"2689_CR33","doi-asserted-by":"publisher","DOI":"10.1115\/1.4010337","author":"W Weibull","year":"1951","unstructured":"Weibull W. A statistical distribution function of wide applicability. J Appl Mech. 1951. https:\/\/doi.org\/10.1115\/1.4010337","journal-title":"J Appl Mech"},{"issue":"2","key":"2689_CR34","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou H, Hastie T. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B: Statistical Methodology. 2005;67(2):301\u201320.","journal-title":"Journal of the Royal Statistical Society Series B: Statistical Methodology"},{"issue":"5","key":"2689_CR35","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001. https:\/\/doi.org\/10.1214\/aos\/1013203451","journal-title":"Ann Stat"},{"key":"2689_CR36","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;45:5\u201332.","journal-title":"Mach Learn"},{"key":"2689_CR37","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2023.1271687","volume":"10","author":"D Zhang","year":"2023","unstructured":"Zhang D, Luan J, Liu B, Yang A, Lv K, Hu P, et al. Comparison of MRI radiomics-based machine learning survival models in predicting prognosis of glioblastoma multiforme. Front Med Lausanne. 2023;10:1271687.","journal-title":"Front Med Lausanne"},{"key":"2689_CR38","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2021.744190","volume":"15","author":"D Pena","year":"2022","unstructured":"Pena D, Suescun J, Schiess M, Ellmore TM, Giancardo L, Initiative AsDN. Toward a multimodal computer-aided diagnostic tool for Alzheimer\u2019s disease conversion. Front Neurosci. 2022;15:744190.","journal-title":"Front Neurosci"},{"key":"2689_CR39","doi-asserted-by":"crossref","unstructured":"Musto H, Stamate D, Pu I, Stahl D: Predicting Alzheimer\u2019s disease diagnosis risk over time with survival machine learning on the ADNI Cohort. In: International Conference on Computational Collective Intelligence. Springer; 2023:700\u201312.","DOI":"10.1007\/978-3-031-41456-5_53"},{"key":"2689_CR40","doi-asserted-by":"crossref","unstructured":"Stamate D, Musto H, Ajnakina O, Stahl D: Predicting risk of dementia with survival machine learning and statistical methods: results on the English longitudinal study of ageing cohort. In: IFIP International Conference on Artificial Intelligence Applications and Innovations. Springer; 2022:436\u201347.","DOI":"10.1007\/978-3-031-08341-9_35"},{"issue":"1","key":"2689_CR41","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-022-01754-y","volume":"22","author":"M Wang","year":"2022","unstructured":"Wang M, Greenberg M, Forkert ND, Chekouo T, Afriyie G, Ismail Z, et al. Dementia risk prediction in individuals with mild cognitive impairment: a comparison of Cox regression and machine learning models. BMC Med Res Methodol. 2022;22(1):284.","journal-title":"BMC Med Res Methodol"},{"key":"2689_CR42","doi-asserted-by":"publisher","DOI":"10.1093\/braincomms\/fcaa057","author":"T Nakagawa","year":"2020","unstructured":"Nakagawa T, Ishida M, Naito J, Nagai A, Yamaguchi S, Onoda K. Prediction of conversion to Alzheimer\u2019s disease using deep survival analysis of MRI images. Brain Commun. 2020. https:\/\/doi.org\/10.1093\/braincomms\/fcaa057","journal-title":"Brain Commu"},{"issue":"1","key":"2689_CR43","doi-asserted-by":"publisher","DOI":"10.1186\/s12967-023-04646-x","volume":"21","author":"X-H Hou","year":"2023","unstructured":"Hou X-H, Suckling J, Shen X-N, Liu Y, Zuo C-T, Huang Y-Y, et al. Multipredictor risk models for predicting individual risk of Alzheimer\u2019s disease. J Transl Med. 2023;21(1):768.","journal-title":"J Transl Med"},{"issue":"1","key":"2689_CR44","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-74399-w","volume":"11","author":"J Venugopalan","year":"2021","unstructured":"Venugopalan J, Tong L, Hassanzadeh HR, Wang MD. Multimodal deep learning models for early detection of Alzheimer\u2019s disease stage. Sci Rep. 2021;11(1):3254.","journal-title":"Sci Rep"},{"issue":"3","key":"2689_CR45","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.3233\/JAD-220021","volume":"87","author":"G Mirabnahrazam","year":"2022","unstructured":"Mirabnahrazam G, Ma D, Lee S, Popuri K, Lee H, Cao J, et al. Machine learning based multimodal neuroimaging genomics dementia score for predicting future conversion to Alzheimer\u2019s disease. J Alzheimers Dis. 2022;87(3):1345\u201365.","journal-title":"J Alzheimers Dis"},{"key":"2689_CR46","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1111\/psyg.12524","volume":"20","author":"L Yin","year":"2020","unstructured":"Yin L, Ren Y, Wang X, Li Y, Hou T, Liu K. The power of the functional activities questionnaire for screening dementia in rural-dwelling older adults at high-risk of cognitive impairment. Psychogeriatrics. 2020;20:427\u201336.","journal-title":"Psychogeriatrics"},{"issue":"4","key":"2689_CR47","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1097\/WAD.0b013e3181e2fc84","volume":"24","author":"E Teng","year":"2010","unstructured":"Teng E, Becker BW, Woo E, Knopman DS, Cummings JL, Lu PH. Utility of the functional activities questionnaire for distinguishing mild cognitive impairment from very mild Alzheimer disease. Alzheimer Dis Assoc Disord. 2010;24(4):348\u201353.","journal-title":"Alzheimer Dis Assoc Disord"},{"issue":"5","key":"2689_CR48","doi-asserted-by":"publisher","first-page":"493","DOI":"10.2174\/156720501205150526115003","volume":"12","author":"GA Marshall","year":"2015","unstructured":"Marshall GA, Zoller AS, Lorius N, Amariglio RE, Locascio JJ, Johnson KA, et al. Functional activities questionnaire items that best discriminate and predict progression from clinically normal to mild cognitive impairment. Curr Alzheimer Res. 2015;12(5):493\u2013502. https:\/\/doi.org\/10.2174\/156720501205150526115003.","journal-title":"Curr Alzheimer Res"},{"issue":"2","key":"2689_CR49","doi-asserted-by":"publisher","DOI":"10.1007\/s12041-021-01309-y","volume":"100","author":"E Fokuoh","year":"2021","unstructured":"Fokuoh E, Xiao D, Fang W, Liu Y, Lu Y, Wang K. Longitudinal analysis of APOE-\u03b54 genotype with the logical memory delayed recall score in Alzheimer\u2019s disease. J Genet. 2021;100(2):60.","journal-title":"J Genet"},{"issue":"2","key":"2689_CR50","doi-asserted-by":"publisher","first-page":"535","DOI":"10.3233\/JAD-230208","volume":"95","author":"S Song","year":"2023","unstructured":"Song S, Asken B, Armstrong MJ, Yang Y, Li Z, Initiative AsDN. Predicting progression to clinical Alzheimer\u2019s disease dementia using the random survival forest. J Alzheimer\u2019s Dis. 2023;95(2):535\u201348.","journal-title":"J Alzheimer\u2019s Dis"},{"issue":"1","key":"2689_CR51","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-83585-3","volume":"11","author":"SH Cho","year":"2021","unstructured":"Cho SH, Woo S, Kim C, Kim HJ, Jang H, Kim BC, et al. Disease progression modelling from preclinical Alzheimer\u2019s disease (AD) to AD dementia. Sci Rep. 2021;11(1):4168.","journal-title":"Sci Rep"},{"issue":"1","key":"2689_CR52","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-96914-3","volume":"11","author":"X Ge","year":"2021","unstructured":"Ge X, Cui K, Liu L, Qin Y, Cui J, Han H, et al. Screening and predicting progression from high-risk mild cognitive impairment to Alzheimer\u2019s disease. Sci Rep. 2021;11(1):17558.","journal-title":"Sci Rep"},{"issue":"1","key":"2689_CR53","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-10202-2","volume":"12","author":"B Bogdanovic","year":"2022","unstructured":"Bogdanovic B, Eftimov T, Simjanoska M. In-depth insights into Alzheimer\u2019s disease by using explainable machine learning approach. Sci Rep. 2022;12(1):6508.","journal-title":"Sci Rep"},{"issue":"10","key":"2689_CR54","doi-asserted-by":"publisher","first-page":"e413","DOI":"10.1097\/RLU.0000000000000547","volume":"39","author":"C Marcus","year":"2014","unstructured":"Marcus C, Mena E, Subramaniam RM. Brain PET in the diagnosis of Alzheimer\u2019s disease. Clin Nucl Med. 2014;39(10):e413\u201326.","journal-title":"Clin Nucl Med"},{"issue":"1","key":"2689_CR55","doi-asserted-by":"publisher","DOI":"10.1186\/s40708-023-00211-w","volume":"10","author":"A Sarica","year":"2023","unstructured":"Sarica A, Aracri F, Bianco MG, Arcuri F, Quattrone A, Quattrone A. Explainability of random survival forests in predicting conversion risk from mild cognitive impairment to Alzheimer\u2019s disease. Brain Inform. 2023;10(1):31.","journal-title":"Brain Inform"},{"key":"2689_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13195-020-00716-0","volume":"12","author":"D Shigemizu","year":"2020","unstructured":"Shigemizu D, Akiyama S, Higaki S, Sugimoto T, Sakurai T, Boroevich KA, et al. Prognosis prediction model for conversion from mild cognitive impairment to Alzheimer\u2019s disease created by integrative analysis of multi-omics data. Alzheimer\u2019s research & therapy. 2020;12:1\u201312.","journal-title":"Alzheimer's research & therapy"},{"issue":"6","key":"2689_CR57","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1016\/j.jalz.2013.11.009","volume":"10","author":"JJ Gomar","year":"2014","unstructured":"Gomar JJ, Conejero-Goldberg C, Davies P, Goldberg TE, Initiative AsDN. Extension and refinement of the predictive value of different classes of markers in ADNI: four-year follow-up data. Alzheimers Dement. 2014;10(6):704\u201312.","journal-title":"Alzheimers Dement"},{"key":"2689_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2018.101637","volume":"21","author":"A S\u00f6rensen","year":"2019","unstructured":"S\u00f6rensen A, Blazhenets G, R\u00fccker G, Schiller F, Meyer PT, Frings L. Prognosis of conversion of mild cognitive impairment to Alzheimer\u2019s dementia by voxel-wise Cox regression based on FDG PET data. NeuroImage: Clinical. 2019;21:101637.","journal-title":"NeuroImage: Clinical"},{"issue":"1","key":"2689_CR59","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-37500-7","volume":"13","author":"N-H Ho","year":"2023","unstructured":"Ho N-H, Jeong Y-H, Kim J. Multimodal multitask learning for predicting MCI to AD conversion using stacked polynomial attention network and adaptive exponential decay. Sci Rep. 2023;13(1):11243.","journal-title":"Sci Rep"},{"key":"2689_CR60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13195-021-00886-5","volume":"13","author":"HV Dansson","year":"2021","unstructured":"Dansson HV, Stempfle L, Egilsd\u00f3ttir H, Schliep A, Portelius E, Blennow K, et al. Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer\u2019s disease. Alzheimer\u2019s Research & Therapy. 2021;13:1\u201316.","journal-title":"Alzheimer's Research & Therapy"}],"container-title":["BMC Medical Research Methodology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12874-025-02689-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12874-025-02689-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12874-025-02689-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T12:04:34Z","timestamp":1762344274000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedresmethodol.biomedcentral.com\/articles\/10.1186\/s12874-025-02689-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,5]]},"references-count":60,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2689"],"URL":"https:\/\/doi.org\/10.1186\/s12874-025-02689-w","relation":{},"ISSN":["1471-2288"],"issn-type":[{"value":"1471-2288","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,5]]},"assertion":[{"value":"24 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was approved by the Ethics Committee of Hamadan University of Medical Sciences (Ethics code: IR.UMSHA.REC.1403.341) and conducted in accordance with the guidelines of the ADNI study and the Declaration of Helsinki. Written informed consent, including permission for data analysis and sharing, was obtained from all participants or their guardians at the time of enrollment.","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":"250"}}