{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T18:53:41Z","timestamp":1775674421272,"version":"3.50.1"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T00:00:00Z","timestamp":1700265600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T00:00:00Z","timestamp":1700265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Brain Inf."],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Random Survival Forests (RSF) has recently showed better performance than statistical survival methods as Cox proportional hazard (CPH) in predicting conversion risk from mild cognitive impairment (MCI) to Alzheimer\u2019s disease (AD). However, RSF application in real-world clinical setting is still limited due to its black-box nature.<\/jats:p><jats:p>For this reason, we aimed at providing a comprehensive study of RSF explainability with SHapley Additive exPlanations (SHAP) on biomarkers of stable and progressive patients (sMCI and pMCI) from Alzheimer\u2019s Disease Neuroimaging Initiative. We evaluated three global explanations\u2014RSF feature importance, permutation importance and SHAP importance\u2014and we quantitatively compared them with Rank-Biased Overlap (RBO). Moreover, we assessed whether multicollinearity among variables may perturb SHAP outcome. Lastly, we stratified pMCI test patients in high, medium and low risk grade, to investigate individual SHAP explanation of one pMCI patient per risk group.<\/jats:p><jats:p>We confirmed that RSF had higher accuracy (0.890) than CPH (0.819), and its stability and robustness was demonstrated by high overlap (RBO\u2009&gt;\u200990%) between feature rankings within first eight features. SHAP local explanations with and without correlated variables had no substantial difference, showing that multicollinearity did not alter the model. FDG, ABETA42 and HCI were the first important features in global explanations, with the highest contribution also in local explanation. FAQ, mPACCdigit, mPACCtrailsB and RAVLT immediate had the highest influence among all clinical and neuropsychological assessments in increasing progression risk, as particularly evident in pMCI patients\u2019 individual explanation. In conclusion, our findings suggest that RSF represents a useful tool to support clinicians in estimating conversion-to-AD risk and that SHAP explainer boosts its clinical utility with intelligible and interpretable individual outcomes that highlights key features associated with AD prognosis.<\/jats:p><jats:p><jats:bold>Graphical Abstract<\/jats:bold><\/jats:p>","DOI":"10.1186\/s40708-023-00211-w","type":"journal-article","created":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T13:02:34Z","timestamp":1700312554000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Explainability of random survival forests in predicting conversion risk from mild cognitive impairment to Alzheimer\u2019s disease"],"prefix":"10.1186","volume":"10","author":[{"given":"Alessia","family":"Sarica","sequence":"first","affiliation":[]},{"given":"Federica","family":"Aracri","sequence":"additional","affiliation":[]},{"given":"Maria Giovanna","family":"Bianco","sequence":"additional","affiliation":[]},{"given":"Fulvia","family":"Arcuri","sequence":"additional","affiliation":[]},{"given":"Andrea","family":"Quattrone","sequence":"additional","affiliation":[]},{"given":"Aldo","family":"Quattrone","sequence":"additional","affiliation":[]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,18]]},"reference":[{"key":"211_CR1","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/j.jalz.2018.02.001","volume":"14","author":"AS Association","year":"2018","unstructured":"Association AS (2018) 2018 Alzheimer\u2019s disease facts and figures. Alzheimer\u2019s Dementia 14:367\u2013429","journal-title":"Alzheimer\u2019s Dementia"},{"key":"211_CR2","doi-asserted-by":"publisher","first-page":"576","DOI":"10.3389\/fnins.2018.00576","volume":"12","author":"A Sarica","year":"2018","unstructured":"Sarica A, Vasta R, Novellino F, Vaccaro MG, Cerasa A, Quattrone A, Initiative ASDN (2018) MRI asymmetry index of hippocampal subfields increases through the continuum from the mild cognitive impairment to the Alzheimer\u2019s disease. Front Neurosci 12:576","journal-title":"Front Neurosci"},{"key":"211_CR3","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1111\/j.1600-0447.2008.01326.x","volume":"119","author":"AJ Mitchell","year":"2009","unstructured":"Mitchell AJ, Shiri-Feshki M (2009) Rate of progression of mild cognitive impairment to dementia\u2013meta-analysis of 41 robust inception cohort studies. Acta Psychiatr Scand 119:252\u2013265","journal-title":"Acta Psychiatr Scand"},{"key":"211_CR4","doi-asserted-by":"publisher","first-page":"329","DOI":"10.3389\/fnagi.2017.00329","volume":"9","author":"A Sarica","year":"2017","unstructured":"Sarica A, Cerasa A, Quattrone A (2017) Random forest algorithm for the classification of neuroimaging data in Alzheimer\u2019s Disease: a systematic review. Front Aging Neurosci 9:329","journal-title":"Front Aging Neurosci"},{"key":"211_CR5","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1016\/j.neuroimage.2015.01.048","volume":"111","author":"EE Bron","year":"2015","unstructured":"Bron EE, Smits M, van der Flier WM, Vrenken H, Barkhof F, Scheltens P, Papma JM, Steketee RM, Mendez Orellana C, Meijboom R, Pinto M, Meireles JR, Garrett C, Bastos-Leite AJ, Abdulkadir A, Ronneberger O, Amoroso N, Bellotti R, Cardenas-Pena D, Alvarez-Meza AM, Dolph CV, Iftekharuddin KM, Eskildsen SF, Coupe P, Fonov VS, Franke K, Gaser C, Ledig C, Guerrero R, Tong T, Gray KR, Moradi E, Tohka J, Routier A, Durrleman S, Sarica A, Di Fatta G, Sensi F, Chincarini A, Smith GM, Stoyanov ZV, Sorensen L, Nielsen M, Tangaro S, Inglese P, Wachinger C, Reuter M, van Swieten JC, Niessen WJ, Klein S (2015) Alzheimer\u2019s disease neuroimaging, I.: Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. Neuroimage 111:562\u2013579","journal-title":"Neuroimage"},{"key":"211_CR6","doi-asserted-by":"publisher","DOI":"10.32604\/csse.2023.036371","author":"H Ahmed","year":"2023","unstructured":"Ahmed H, Soliman H, El-Sappagh S, Abuhmed T, Elmogy M (2023) Early detection of Alzheimer\u2019s disease based on laplacian re-decomposition and XGBoosting. Comput Syst Sci Eng. https:\/\/doi.org\/10.3260\/csse.2023.036371","journal-title":"Comput Syst Sci Eng"},{"key":"211_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2018.03.011","author":"A Sarica","year":"2018","unstructured":"Sarica A, Cerasa A, Quattrone A, Calhoun V (2018) Editorial on special issue: machine learning on MCI. J Neurosci Methods. https:\/\/doi.org\/10.1016\/j.jneumeth.2018.03.011","journal-title":"J Neurosci Methods"},{"key":"211_CR8","doi-asserted-by":"publisher","first-page":"14487","DOI":"10.1007\/s00521-022-07263-9","volume":"34","author":"S El-Sappagh","year":"2022","unstructured":"El-Sappagh S, Saleh H, Ali F, Amer E, Abuhmed T (2022) Two-stage deep learning model for Alzheimer\u2019s disease detection and prediction of the mild cognitive impairment time. Neural Comput Appl 34:14487\u201314509","journal-title":"Neural Comput Appl"},{"key":"211_CR9","doi-asserted-by":"crossref","unstructured":"Sarica, A., Quattrone, A., Quattrone, A.: Explainable boosting machine for predicting Alzheimer\u2019s disease from mri hippocampal subfields. In: Brain Informatics: 14th International Conference, BI 2021, Virtual Event, September 17\u201319, 2021, Proceedings 14, pp. 341\u2013350. Springer","DOI":"10.1007\/978-3-030-86993-9_31"},{"key":"211_CR10","doi-asserted-by":"publisher","first-page":"2660","DOI":"10.1038\/s41598-021-82098-3","volume":"11","author":"S El-Sappagh","year":"2021","unstructured":"El-Sappagh S, Alonso JM, Islam SR, Sultan AM, Kwak KS (2021) A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer\u2019s disease. Sci Rep-Uk 11:2660","journal-title":"Sci Rep-Uk"},{"key":"211_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jneumeth.2018.03.011","volume":"302","author":"A Sarica","year":"2018","unstructured":"Sarica A, Cerasa A, Quattrone A, Calhoun V (2018) Editorial on special issue: machine learning on MCI. J Neurosci Methods 302:1\u20132","journal-title":"J Neurosci Methods"},{"key":"211_CR12","doi-asserted-by":"publisher","first-page":"1850909","DOI":"10.1155\/2017\/1850909","volume":"2017","author":"P Battista","year":"2017","unstructured":"Battista P, Salvatore C, Castiglioni I (2017) Optimizing neuropsychological assessments for cognitive, behavioral, and functional impairment classification: a machine learning study. Behav Neurol 2017:1850909","journal-title":"Behav Neurol"},{"key":"211_CR13","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1016\/j.neuroimage.2008.07.013","volume":"43","author":"X Hua","year":"2008","unstructured":"Hua X, Leow AD, Parikshak N, Lee S, Chiang MC, Toga AW, Jack CR Jr, Weiner MW, Thompson PM (2008) Tensor-based morphometry as a neuroimaging biomarker for Alzheimer\u2019s disease: an MRI study of 676 AD, MCI, and normal subjects. Neuroimage 43:458\u2013469","journal-title":"Neuroimage"},{"key":"211_CR14","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.compbiomed.2015.01.003","volume":"58","author":"C Cabral","year":"2015","unstructured":"Cabral C, Morgado PM, Campos Costa D, Silveira M (2015) Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages. Comput Biol Med 58:101\u2013109","journal-title":"Comput Biol Med"},{"key":"211_CR15","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.neuroimage.2011.01.049","volume":"56","author":"K Chen","year":"2011","unstructured":"Chen K, Ayutyanont N, Langbaum JB, Fleisher AS, Reschke C, Lee W, Liu X, Bandy D, Alexander GE, Thompson PM, Shaw L, Trojanowski JQ, Jack CR Jr, Landau SM, Foster NL, Harvey DJ, Weiner MW, Koeppe RA, Jagust WJ, Reiman EM (2011) Characterizing Alzheimer\u2019s disease using a hypometabolic convergence index. Neuroimage 56:52\u201360","journal-title":"Neuroimage"},{"key":"211_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s12276-019-0299-y","volume":"51","author":"JC Lee","year":"2019","unstructured":"Lee JC, Kim SJ, Hong S, Kim Y (2019) Diagnosis of Alzheimer\u2019s disease utilizing amyloid and tau as fluid biomarkers. Exp Mol Med 51:1\u201310","journal-title":"Exp Mol Med"},{"key":"211_CR17","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1038\/ng.2802","volume":"45","author":"JC Lambert","year":"2013","unstructured":"Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, DeStafano AL, Bis JC, Beecham GW, Grenier-Boley B, Russo G, Thorton-Wells TA, Jones N, Smith AV, Chouraki V, Thomas C, Ikram MA, Zelenika D, Vardarajan BN, Kamatani Y, Lin CF, Gerrish A, Schmidt H, Kunkle B, Dunstan ML, Ruiz A, Bihoreau MT, Choi SH, Reitz C, Pasquier F, Cruchaga C, Craig D, Amin N, Berr C, Lopez OL, De Jager PL, Deramecourt V, Johnston JA, Evans D, Lovestone S, Letenneur L, Moron FJ, Rubinsztein DC, Eiriksdottir G, Sleegers K, Goate AM, Fievet N, Huentelman MW, Gill M, Brown K, Kamboh MI, Keller L, Barberger-Gateau P, McGuiness B, Larson EB, Green R, Myers AJ, Dufouil C, Todd S, Wallon D, Love S, Rogaeva E, Gallacher J, St George-Hyslop P, Clarimon J, Lleo A, Bayer A, Tsuang DW, Yu L, Tsolaki M, Bossu P, Spalletta G, Proitsi P, Collinge J, Sorbi S, Sanchez-Garcia F, Fox NC, Hardy J, Deniz Naranjo MC, Bosco P, Clarke R, Brayne C, Galimberti D, Mancuso M, Matthews F, Moebus S, Mecocci P, Del Zompo M, Maier W, Hampel H, Pilotto A, Bullido M, Panza F, Caffarra P, Nacmias B, Gilbert JR, Mayhaus M, Lannefelt L, Hakonarson H, Pichler S, Carrasquillo MM, Ingelsson M, Beekly D, Alvarez V, Zou F, Valladares O, Younkin SG, Coto E, Hamilton-Nelson KL, Gu W, Razquin C, Pastor P, Mateo I, Owen MJ, Faber KM, Jonsson PV, Combarros O, O\u2019Donovan MC, Cantwell LB, Soininen H, Blacker D, Mead S, Mosley TH Jr, Bennett DA, Harris TB, Fratiglioni L, Holmes C, de Bruijn RF, Passmore P, Montine TJ, Bettens K, Rotter JI, Brice A, Morgan K, Foroud TM, Kukull WA, Hannequin D, Powell JF, Nalls MA, Ritchie K, Lunetta KL, Kauwe JS, Boerwinkle E, Riemenschneider M, Boada M, Hiltuenen M, Martin ER, Schmidt R, Rujescu D, Wang LS, Dartigues JF, Mayeux R, Tzourio C, Hofman A, Nothen MM, Graff C, Psaty BM, Jones L, Haines JL, Holmans PA, Lathrop M, Pericak-Vance MA, Launer LJ, Farrer LA, van Duijn CM, Van Broeckhoven C, Moskvina V, Seshadri S, Williams J, Schellenberg GD, Amouyel P (2013) Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer\u2019s disease. Nat Genet 45:1452\u20131458","journal-title":"Nat Genet"},{"key":"211_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/b97377","volume-title":"Survival analysis: techniques for censored and truncated data","author":"JP Klein","year":"2003","unstructured":"Klein JP, Moeschberger ML (2003) Survival analysis: techniques for censored and truncated data. Springer, New York"},{"key":"211_CR19","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","volume":"34","author":"DR Cox","year":"1972","unstructured":"Cox DR (1972) Regression models and life-tables. J Roy Stat Soc Ser B 34:187\u2013202","journal-title":"J Roy Stat Soc Ser B"},{"key":"211_CR20","doi-asserted-by":"publisher","first-page":"20410","DOI":"10.1038\/s41598-020-77220-w","volume":"10","author":"A Spooner","year":"2020","unstructured":"Spooner A, Chen E, Sowmya A, Sachdev P, Kochan NA, Trollor J, Brodaty H (2020) A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction. Sci Rep 10:20410","journal-title":"Sci Rep"},{"key":"211_CR21","first-page":"135","volume":"10","author":"J Orozco-Sanchez","year":"2019","unstructured":"Orozco-Sanchez J, Trevino V, Martinez-Ledesma E, Farber J, Tamez-Pe\u00f1a J (2019) Exploring survival models associated with MCI to AD conversion: a machine learning approach. BioRxiv 10:135","journal-title":"BioRxiv"},{"key":"211_CR22","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45:5\u201332","journal-title":"Mach Learn"},{"key":"211_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s00432-022-04063-5","author":"JO Jung","year":"2022","unstructured":"Jung JO, Crnovrsanin N, Wirsik NM, Nienhuser H, Peters L, Popp F, Schulze A, Wagner M, Muller-Stich BP, Buchler MW, Schmidt T (2022) Machine learning for optimized individual survival prediction in resectable upper gastrointestinal cancer. J Cancer Res Clin Oncol. https:\/\/doi.org\/10.1007\/s00432-022-04063-5","journal-title":"J Cancer Res Clin Oncol"},{"key":"211_CR24","first-page":"104","volume":"55","author":"Z Chen","year":"2021","unstructured":"Chen Z, Xu H, Li Z, Zhang Y, Zhou T, You W, Pan K, Li W (2021) Random survival forest: applying machine learning algorithm in survival analysis of biomedical data. Zhonghua Yu Fang Yi Xue Za Zhi 55:104\u2013109","journal-title":"Zhonghua Yu Fang Yi Xue Za Zhi"},{"key":"211_CR25","doi-asserted-by":"publisher","first-page":"97","DOI":"10.3390\/a15030097","volume":"15","author":"A Sarica","year":"2022","unstructured":"Sarica A (2022) Editorial for the special issue on \u201cmachine learning in healthcare and biomedical application.\u201d Algorithms 15:97","journal-title":"Algorithms"},{"key":"211_CR26","volume-title":"International conference on brain informatics","author":"A Sarica","year":"2023","unstructured":"Sarica A, Aracri F, Bianco MG, Vaccaro MG, Quattrone A, Quattrone A (2023) Conversion from mild cognitive impairment to Alzheimer\u2019s disease: a comparison of tree-based machine learning algorithms for survival analysis. In: Feng Liu Yu, Zhang HK, Stephen EP, Wang H (eds) International conference on brain informatics. Springer, Cham"},{"key":"211_CR27","doi-asserted-by":"publisher","DOI":"10.1214\/08-AOAS169","author":"H Ishwaran","year":"2008","unstructured":"Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS (2008) Random survival forests. Ann Appl Stat. https:\/\/doi.org\/10.1214\/08-AOAS169","journal-title":"Ann Appl Stat"},{"key":"211_CR28","doi-asserted-by":"publisher","first-page":"1272","DOI":"10.1002\/sim.7212","volume":"36","author":"MN Wright","year":"2017","unstructured":"Wright MN, Dankowski T, Ziegler A (2017) Unbiased split variable selection for random survival forests using maximally selected rank statistics. Stat Med 36:1272\u20131284","journal-title":"Stat Med"},{"key":"211_CR29","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63:3\u201342","journal-title":"Mach Learn"},{"key":"211_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-41456-5_53","author":"H Musto","year":"2023","unstructured":"Musto H, Stamate D, Pu I, Stahl D (2023) Predicting Alzheimers disease diagnosis risk over time with survival machine learning on the ADNI Cohort. arXiv Preprint. https:\/\/doi.org\/10.1007\/978-3-031-41456-5_53","journal-title":"arXiv Preprint"},{"key":"211_CR31","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-230208","author":"S Song","year":"2023","unstructured":"Song S, Asken B, Armstrong MJ, Yang Y, Li Z (2023) Predicting progression to clinical Alzheimer\u2019s disease dementia using the random survival forest. J Alzheimer\u2019s Dis. https:\/\/doi.org\/10.3233\/JAD-230208","journal-title":"J Alzheimer\u2019s Dis"},{"key":"211_CR32","unstructured":"Molnar C. Interpretable machine learning. Lulu.com (2020)"},{"key":"211_CR33","unstructured":"Molnar C. Interpreting machine learning models with SHAP. Lulu.com (2023)"},{"key":"211_CR34","doi-asserted-by":"publisher","first-page":"101805","DOI":"10.1016\/j.inffus.2023.101805","volume":"99","author":"S Ali","year":"2023","unstructured":"Ali S, Abuhmed T, El-Sappagh S, Muhammad K, Alonso-Moral JM, Confalonieri R, Guidotti R, Del Ser J, D\u00edaz-Rodr\u00edguez N, Herrera F (2023) Explainable artificial intelligence (XAI): what we know and what is left to attain trustworthy artificial intelligence. Inform Fusion 99:101805","journal-title":"Inform Fusion"},{"key":"211_CR35","doi-asserted-by":"crossref","unstructured":"Sarica, A., Quattrone, A., Quattrone, A.: Introducing the Rank-Biased Overlap as Similarity Measure for Feature Importance in Explainable Machine Learning: A Case Study on Parkinson\u2019s Disease. In: Brain Informatics: 15th International Conference, BI 2022, Padua, Italy. 2022, Proceedings, pp. 129\u2013139. Springer","DOI":"10.1007\/978-3-031-15037-1_11"},{"key":"211_CR36","doi-asserted-by":"publisher","first-page":"2188","DOI":"10.1007\/s11682-022-00688-9","volume":"16","author":"A Sarica","year":"2022","unstructured":"Sarica A, Quattrone A, Quattrone A (2022) Explainable machine learning with pairwise interactions for the classification of Parkinson\u2019s disease and SWEDD from clinical and imaging features. Brain Imaging Behav 16:2188\u20132198","journal-title":"Brain Imaging Behav"},{"key":"211_CR37","doi-asserted-by":"publisher","DOI":"10.4855\/arXiv.1904.12991","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Song K, Sun Y, Tan S, Udell M (2019) \u201c Why should you trust my explanation?\u201d understanding uncertainty in LIME explanations. arXiv Preprint. https:\/\/doi.org\/10.4855\/arXiv.1904.12991","journal-title":"arXiv Preprint"},{"key":"211_CR38","unstructured":"Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Adv Neur In. (2017)"},{"key":"211_CR39","doi-asserted-by":"publisher","first-page":"6968","DOI":"10.1038\/s41598-021-86327-7","volume":"11","author":"A Moncada-Torres","year":"2021","unstructured":"Moncada-Torres A, van Maaren MC, Hendriks MP, Siesling S, Geleijnse G (2021) Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival. Sci Rep 11:6968","journal-title":"Sci Rep"},{"key":"211_CR40","doi-asserted-by":"publisher","DOI":"10.1186\/s12902-023-01368-5","author":"LZ Xu","year":"2023","unstructured":"Xu LZ, Cai LC, Zhu Z, Chen G (2023) Comparison of the cox regression to machine learning in predicting the survival of anaplastic thyroid carcinoma. Bmc Endocr Disord. https:\/\/doi.org\/10.1186\/s12902-023-01368-5","journal-title":"Bmc Endocr Disord"},{"key":"211_CR41","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2023.1219586","author":"PA Moreno-Sanchez","year":"2023","unstructured":"Moreno-Sanchez PA (2023) Improvement of a prediction model for heart failure survival through explainable artificial intelligence. Front Cardiovasc Med. https:\/\/doi.org\/10.3389\/fcvm.2023.1219586","journal-title":"Front Cardiovasc Med"},{"key":"211_CR42","doi-asserted-by":"crossref","unstructured":"Arya V, Bellamy RK, Chen P-Y, Dhurandhar A, Hind M, Hoffman, SC, Houde S, Liao QV, Luss R, Mojsilovi\u0107 A. AI Explainability 360 Toolkit. In: Proceedings of the 3rd ACM India Joint International Conference on Data Science & Management of Data (8th ACM IKDD CODS & 26th COMAD), pp. 376\u2013379","DOI":"10.1145\/3430984.3430987"},{"key":"211_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1852102.1852106","volume":"28","author":"W Webber","year":"2010","unstructured":"Webber W, Moffat A, Zobel J (2010) A similarity measure for indefinite rankings. ACM Trans Inform Syst (TOIS) 28:1\u201338","journal-title":"ACM Trans Inform Syst (TOIS)"},{"key":"211_CR44","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, Initiative ASDN (2020) Prediction of conversion to Alzheimer\u2019s disease using deep survival analysis of MRI images. Brain Commun. https:\/\/doi.org\/10.1093\/braincomms\/fcaa057","journal-title":"Brain Commun"},{"key":"211_CR45","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, Cao J, Galvin JE, Wang L, Beg MF (2023) Predicting time-to-conversion for dementia of Alzheimer\u2019s type using multi-modal deep survival analysis. Neurobiol Aging 121:139\u2013156","journal-title":"Neurobiol Aging"},{"key":"211_CR46","doi-asserted-by":"crossref","unstructured":"Sarica A, Di Fatta G, Cannataro M. K-Surfer: a KNIME extension for the management and analysis of human brain MRI FreeSurfer\/FSL data. In: Brain Informatics and Health: International Conference, BIH 2014, Warsaw, Poland. 2014. Proceedings, pp. 481\u2013492. Springer","DOI":"10.1007\/978-3-319-09891-3_44"},{"key":"211_CR47","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.3233\/JAD-201370","volume":"80","author":"CB Wright","year":"2021","unstructured":"Wright CB, DeRosa JT, Moon MP, Strobino K, DeCarli C, Cheung YK, Assuras S, Levin B, Stern Y, Sun X (2021) Race\/ethnic disparities in mild cognitive impairment and dementia: the Northern Manhattan Study. J Alzheimers Dis 80:1129\u20131138","journal-title":"J Alzheimers Dis"},{"key":"211_CR48","doi-asserted-by":"publisher","first-page":"1656","DOI":"10.3390\/app13031656","volume":"13","author":"YJ Parra Bautista","year":"2023","unstructured":"Parra Bautista YJ, Messeha SS, Theran C, Al\u00f3 R, Yedjou C, Adankai V, Babatunde S, Evolution ASDPOL (2023) Marital status of never married with Rey auditory verbal learning test cognition performance is associated with mild cognitive impairment. Appl Sci 13:1656","journal-title":"Appl Sci"},{"key":"211_CR49","first-page":"746","volume":"67","author":"SE O'Bryant","year":"2010","unstructured":"O\u2019Bryant SE, Lacritz LH, Hall J, Waring SC, Chan W, Khodr ZG, Massman PJ, Hobson V, Cullum CM (2010) Validation of the new interpretive guidelines for the clinical dementia rating scale sum of boxes score in the national Alzheimer\u2019s coordinating center database. Arch Neurol 67:746\u2013749","journal-title":"Arch Neurol"},{"key":"211_CR50","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1093\/geronj\/37.3.323","volume":"37","author":"RI Pfeffer","year":"1982","unstructured":"Pfeffer RI, Kurosaki TT, Harrah CH Jr, Chance JM, Filos S (1982) Measurement of functional activities in older adults in the community. J Gerontol 37:323\u2013329","journal-title":"J Gerontol"},{"key":"211_CR51","doi-asserted-by":"publisher","first-page":"756","DOI":"10.3389\/fneur.2019.00756","volume":"10","author":"M Grassi","year":"2019","unstructured":"Grassi M, Rouleaux N, Caldirola D, Loewenstein D, Schruers K, Perna G, Dumontier M (2019) A novel ensemble-based machine learning algorithm to predict the conversion from mild cognitive impairment to Alzheimer\u2019s disease using socio-demographic characteristics, clinical information, and neuropsychological measures. Front Neurol 10:756","journal-title":"Front Neurol"},{"key":"211_CR52","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/0022-3956(75)90026-6","volume":"12","author":"MF Folstein","year":"1975","unstructured":"Folstein MF, Folstein SE, McHugh PR (1975) \u201cMini-mental state\u201d. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12:189\u2013198","journal-title":"J Psychiatr Res"},{"key":"211_CR53","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1002\/gps.1010","volume":"18","author":"A Est\u00e9vez-Gonz\u00e1lez","year":"2003","unstructured":"Est\u00e9vez-Gonz\u00e1lez A, Kulisevsky J, Boltes A, Oterm\u00edn P, Garc\u00eda-S\u00e1nchez C (2003) Rey verbal learning test is a useful tool for differential diagnosis in the preclinical phase of Alzheimer\u2019s disease: comparison with mild cognitive impairment and normal aging. Int J Geriatr Psychiatry 18:1021\u20131028","journal-title":"Int J Geriatr Psychiatry"},{"key":"211_CR54","doi-asserted-by":"publisher","first-page":"271","DOI":"10.2466\/pms.1958.8.3.271","volume":"8","author":"RM Reitan","year":"1958","unstructured":"Reitan RM (1958) Validity of the trail making test as an indicator of organic brain damage. Percept Mot Skills 8:271\u2013276","journal-title":"Percept Mot Skills"},{"key":"211_CR55","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1001\/jamaneurol.2014.803","volume":"71","author":"MC Donohue","year":"2014","unstructured":"Donohue MC, Sperling RA, Salmon DP, Rentz DM, Raman R, Thomas RG, Weiner M, Aisen PS (2014) The preclinical Alzheimer cognitive composite: measuring amyloid-related decline. JAMA Neurol 71:961\u2013970","journal-title":"JAMA Neurol"},{"key":"211_CR56","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1002\/ana.21610","volume":"65","author":"LM Shaw","year":"2009","unstructured":"Shaw LM, Vanderstichele H, Knapik-Czajka M, Clark CM, Aisen PS, Petersen RC, Blennow K, Soares H, Simon A, Lewczuk P, Dean R, Siemers E, Potter W, Lee VM, Trojanowski JQ (2009) Cerebrospinal fluid biomarker signature in Alzheimer\u2019s disease neuroimaging initiative subjects. Ann Neurol 65:403\u2013413","journal-title":"Ann Neurol"},{"key":"211_CR57","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1006\/nimg.1998.0395","volume":"9","author":"AM Dale","year":"1999","unstructured":"Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9:179\u2013194","journal-title":"Neuroimage"},{"key":"211_CR58","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1212\/WNL.0b013e3181e8e8b8","volume":"75","author":"SM Landau","year":"2010","unstructured":"Landau SM, Harvey D, Madison CM, Reiman EM, Foster NL, Aisen PS, Petersen RC, Shaw LM, Trojanowski JQ, Jack CR Jr, Weiner MW, Jagust WJ (2010) Comparing predictors of conversion and decline in mild cognitive impairment. Neurology 75:230\u2013238","journal-title":"Neurology"},{"key":"211_CR59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-020-00305-w","volume":"7","author":"JT Hancock","year":"2020","unstructured":"Hancock JT, Khoshgoftaar TM (2020) Survey on categorical data for neural networks. J Big Data 7:1\u201341","journal-title":"J Big Data"},{"key":"211_CR60","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1093\/bioinformatics\/btr597","volume":"28","author":"DJ Stekhoven","year":"2012","unstructured":"Stekhoven DJ, Buhlmann P (2012) MissForest\u2013non-parametric missing value imputation for mixed-type data. Bioinformatics 28:112\u2013118","journal-title":"Bioinformatics"},{"key":"211_CR61","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), pp. 684\u2013688. IEEE","DOI":"10.1109\/CBMS58004.2023.00300"},{"key":"211_CR62","doi-asserted-by":"publisher","unstructured":"Aracri F, Bianco MG, Quattrone A, Sarica A (2023) Impact of imputation methods on supervised classification: a multiclass study on patients with parkinson's disease and subjects with scans without evidence of dopaminergic deficit. 2023 International Workshop on Biomedical Applications, Technologies and Sensors (BATS), Catanzaro, Italy, 2023, pp. 28\u201332, https:\/\/doi.org\/10.1109\/BATS59463.2023.10303151","DOI":"10.1109\/BATS59463.2023.10303151"},{"key":"211_CR63","doi-asserted-by":"publisher","first-page":"1056","DOI":"10.1016\/j.spl.2010.02.020","volume":"80","author":"H Ishwaran","year":"2010","unstructured":"Ishwaran H, Kogalur UB (2010) Consistency of random survival forests. Stat Probab Lett 80:1056\u20131064","journal-title":"Stat Probab Lett"},{"key":"211_CR64","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1016\/j.jacr.2018.02.026","volume":"15","author":"HB Harvey","year":"2018","unstructured":"Harvey HB, Sotardi ST (2018) The pareto principle. J Am Coll Radiol 15:931","journal-title":"J Am Coll Radiol"},{"key":"211_CR65","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1002\/sim.4154","volume":"30","author":"H Uno","year":"2011","unstructured":"Uno H, Cai T, Pencina MJ, D\u2019Agostino RB, Wei L-J (2011) On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat Med 30:1105\u20131117","journal-title":"Stat Med"},{"key":"211_CR66","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1097\/EDE.0b013e3181c30fb2","volume":"21","author":"EW Steyerberg","year":"2010","unstructured":"Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, Pencina MJ, Kattan MW (2010) Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21:128\u2013138","journal-title":"Epidemiology"},{"key":"211_CR67","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1080\/01621459.1958.10501452","volume":"53","author":"EL Kaplan","year":"1958","unstructured":"Kaplan EL, Meier P (1958) Nonparametric estimation from incomplete observations. J Am Stat Assoc 53:457\u2013481","journal-title":"J Am Stat Assoc"},{"key":"211_CR68","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1198\/106186008X344522","volume":"17","author":"M Sandri","year":"2008","unstructured":"Sandri M, Zuccolotto P (2008) A bias correction algorithm for the Gini variable importance measure in classification trees. J Comput Graph Stat 17:611\u2013628","journal-title":"J Comput Graph Stat"},{"key":"211_CR69","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1186\/s13195-019-0512-1","volume":"11","author":"YN Ou","year":"2019","unstructured":"Ou YN, Xu W, Li JQ, Guo Y, Cui M, Chen KL, Huang YY, Dong Q, Tan L, Yu JT (2019) FDG-PET as an independent biomarker for Alzheimer\u2019s biological diagnosis: a longitudinal study. Alzheimers Res Ther 11:57","journal-title":"Alzheimers Res Ther"},{"key":"211_CR70","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/S1474-4422(06)70355-6","volume":"5","author":"O Hansson","year":"2006","unstructured":"Hansson O, Zetterberg H, Buchhave P, Londos E, Blennow K, Minthon L (2006) Association between CSF biomarkers and incipient Alzheimer\u2019s disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol 5:228\u2013234","journal-title":"Lancet Neurol"},{"key":"211_CR71","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 (2010) Utility of the functional activities questionnaire for distinguishing mild cognitive impairment from very mild Alzheimer\u2019s disease. Alzheimer Dis Assoc Disord 24:348","journal-title":"Alzheimer Dis Assoc Disord"},{"key":"211_CR72","doi-asserted-by":"publisher","first-page":"110234","DOI":"10.1016\/j.knosys.2022.110234","volume":"262","author":"M Krzyzi\u0144ski","year":"2023","unstructured":"Krzyzi\u0144ski M, Spytek M, Baniecki H, Biecek P (2023) SurvSHAP (t): time-dependent explanations of machine learning survival models. Knowl-Based Syst 262:110234","journal-title":"Knowl-Based Syst"},{"key":"211_CR73","doi-asserted-by":"publisher","first-page":"106164","DOI":"10.1016\/j.knosys.2020.106164","volume":"203","author":"MS Kovalev","year":"2020","unstructured":"Kovalev MS, Utkin LV, Kasimov EM (2020) SurvLIME: a method for explaining machine learning survival models. Knowl-Based Syst 203:106164","journal-title":"Knowl-Based Syst"}],"container-title":["Brain Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-023-00211-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40708-023-00211-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-023-00211-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T05:51:26Z","timestamp":1730526686000},"score":1,"resource":{"primary":{"URL":"https:\/\/braininformatics.springeropen.com\/articles\/10.1186\/s40708-023-00211-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,18]]},"references-count":73,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["211"],"URL":"https:\/\/doi.org\/10.1186\/s40708-023-00211-w","relation":{},"ISSN":["2198-4018","2198-4026"],"issn-type":[{"value":"2198-4018","type":"print"},{"value":"2198-4026","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,18]]},"assertion":[{"value":"29 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2023","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 authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"31"}}