{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T21:24:09Z","timestamp":1775856249101,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T00:00:00Z","timestamp":1690588800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T00:00:00Z","timestamp":1690588800000},"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":["Appl Intell"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s10489-023-04850-8","type":"journal-article","created":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T12:01:20Z","timestamp":1690632080000},"page":"24876-24894","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Kernelized multi-granulation fuzzy rough set over hybrid attribute decision system and application to stroke risk prediction"],"prefix":"10.1007","volume":"53","author":[{"given":"Ting","family":"Wang","sequence":"first","affiliation":[]},{"given":"Bingzhen","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,29]]},"reference":[{"issue":"2","key":"4850_CR1","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/s13042-021-01404-7","volume":"13","author":"J Ba","year":"2022","unstructured":"Ba J, Liu K, Ju H, Xu S, Xu T, Yang X (2022) Triple-g: a new mgrs and attribute reduction. Int J Mach Learn Cybern 13(2):337\u2013356","journal-title":"Int J Mach Learn Cybern"},{"key":"4850_CR2","doi-asserted-by":"crossref","first-page":"108127","DOI":"10.1016\/j.asoc.2021.108127","volume":"114","author":"J Bai","year":"2022","unstructured":"Bai J, Sun B, Chu X, Wang T, Li H, Huang Q (2022) Neighborhood rough set-based multi-attribute prediction approach and its application of gout patients. Appl Soft Comput 114:108127","journal-title":"Appl Soft Comput"},{"issue":"163","key":"4850_CR3","doi-asserted-by":"crossref","first-page":"107922","DOI":"10.1016\/j.measurement.2020.107922","volume":"15","author":"VR Balaji","year":"2020","unstructured":"Balaji VR, Suganthi ST, Rajadevi R, Krishna Kumar V, Saravana Balaji B, Pandiyan S (2020) Skin disease detection and segmentation using dynamic graph cut algorithm and classification through naive bayes classifier. Meas 15(163):107922","journal-title":"Meas"},{"issue":"8","key":"4850_CR4","doi-asserted-by":"crossref","first-page":"2886","DOI":"10.1109\/TFUZZ.2021.3096212","volume":"30","author":"J Chen","year":"2021","unstructured":"Chen J, Lin Y, Mi J, Li S, Ding W (2021) A spectral feature selection approach with kernelized fuzzy rough sets. IEEE Trans Fuzzy Syst 30(8):2886\u20132901","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"1","key":"4850_CR5","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Infor Theor 13(1):21\u201327","journal-title":"IEEE Trans Infor Theor"},{"key":"4850_CR6","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.ins.2015.09.007","volume":"329","author":"J Derrac","year":"2016","unstructured":"Derrac J, Chiclana F, Garc\u00eda S, Herrera F (2016) Evolutionary fuzzy k-nearest neighbors algorithm using interval-valued fuzzy sets. Inf Sci 329:144\u2013163","journal-title":"Inf Sci"},{"key":"4850_CR7","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1080\/03081079008935107","volume":"17","author":"D Dubois","year":"1990","unstructured":"Dubois D, Prade H (1990) Rough fuzzy sets and fuzzy rough sets. Int J Gen Syst 17:191\u2013209","journal-title":"Int J Gen Syst"},{"key":"4850_CR8","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.eswa.2017.03.019","volume":"80","author":"S Ezghari","year":"2017","unstructured":"Ezghari S, Zahi A, Zenkouar K (2017) A new nearest neighbor classification method based on fuzzy set theory and aggregation operators. Exp Syst Appl 80:58\u201374","journal-title":"Exp Syst Appl"},{"issue":"5","key":"4850_CR9","doi-asserted-by":"crossref","first-page":"1835","DOI":"10.1109\/TCYB.2018.2815178","volume":"49","author":"H Fujita","year":"2019","unstructured":"Fujita H, Gaeta A, Loia V, Orciuoli F (2019) Resilience analysis of critical infrastructures: a cognitive approach based on granular computing. IEEE trans Cybern 49(5):1835\u20131848","journal-title":"IEEE trans Cybern"},{"issue":"5","key":"4850_CR10","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1109\/TFUZZ.2019.2955047","volume":"28","author":"H Fujita","year":"2020","unstructured":"Fujita H, Gaeta A, Loia V, Orciuoli F (2020) Hypotheses analysis and assessment in counterterrorism activities: A method based on owa and fuzzy probabilistic rough sets. IEEE Trans Fuzzy Syst 28(5):831\u2013845","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"4850_CR11","doi-asserted-by":"crossref","first-page":"107329","DOI":"10.1016\/j.asoc.2021.107329","volume":"106","author":"V Goreke","year":"2021","unstructured":"Goreke V, Sari V, Kockanat S (2021) A novel classifier architecture based on deep neural network for covid-19 detection using laboratory findings. Appl Soft Comput 106:107329","journal-title":"Appl Soft Comput"},{"key":"4850_CR12","doi-asserted-by":"crossref","first-page":"101858","DOI":"10.1016\/j.artmed.2020.101858","volume":"107","author":"D Gu","year":"2020","unstructured":"Gu D, Su K, Zhao H (2020) A case-based ensemble learning system for explainable breast cancer recurrence prediction. Artif Intell Med 107:101858","journal-title":"Artif Intell Med"},{"issue":"18","key":"4850_CR13","doi-asserted-by":"crossref","first-page":"3577","DOI":"10.1016\/j.ins.2008.05.024","volume":"178","author":"Q Hu","year":"2008","unstructured":"Hu Q, Yu D, Liu J, Wu C (2008) Neighborhood rough set based heterogeneous feature subset selection. Inf Sci 178(18):3577\u20133594","journal-title":"Inf Sci"},{"issue":"18","key":"4850_CR14","doi-asserted-by":"crossref","first-page":"3577","DOI":"10.1016\/j.ins.2008.05.024","volume":"178","author":"Q Hu","year":"2008","unstructured":"Hu Q, Yu D, Liu J, Wu C (2008) Neighborhood rough set based heterogeneous feature subset selection. Inf Sci 178(18):3577\u20133594","journal-title":"Inf Sci"},{"issue":"11","key":"4850_CR15","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1109\/TKDE.2010.260","volume":"23","author":"Q Hu","year":"2011","unstructured":"Hu Q, Yu D, Pedrycz W, Chen D (2011) Kernelized fuzzy rough sets and their applications. IEEE Trans Knowl Data Eng 23(11):1649\u20131667","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"4850_CR16","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1109\/TFUZZ.2011.2181180","volume":"20","author":"Q Hu","year":"2012","unstructured":"Hu Q, Zhang L, An S, Zhang D, Yu D (2012) On robust fuzzy rough set models. IEEE Trans Fuzzy Syst 20(4):636\u2013651","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"1","key":"4850_CR17","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1109\/TFUZZ.2017.2647966","volume":"26","author":"Q Hu","year":"2018","unstructured":"Hu Q, Zhang L, Zhou Y, Pedrycz W (2018) Large-scale multimodality attribute reduction with multi-kernel fuzzy rough sets. IEEE Trans Fuzzy Syst 26(1):226\u2013238","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"5","key":"4850_CR18","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/S1474-4422(19)30034-1","volume":"18","author":"CO Johnson","year":"2019","unstructured":"Johnson CO, Nguyen M (2019) Global, regional, and national burden of stroke, 1990\u20132016: a systematic analysis for the global burden of disease study 2016. Lancet Neurol 18(5):439\u2013458","journal-title":"Lancet Neurol"},{"key":"4850_CR19","doi-asserted-by":"crossref","first-page":"107612","DOI":"10.1016\/j.asoc.2021.107612","volume":"110","author":"H Ju","year":"2021","unstructured":"Ju H, Ding W, Yang X, Fujita H, Xu S (2021) Robust supervised rough granular description model with the principle of justifiable granularity. Appl Soft Comput 110:107612","journal-title":"Appl Soft Comput"},{"key":"4850_CR20","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1109\/TSMC.1985.6313426","volume":"4","author":"JM Keller","year":"1985","unstructured":"Keller JM, Gray MR, Givens JA (1985) A fuzzy k-nearest neighbor algorithm. IEEE Trans Syst Man Cybern 4:580\u2013585","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"4850_CR21","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.knosys.2015.07.024","volume":"91","author":"J Li","year":"2016","unstructured":"Li J, Ren Y, Mei C, Qian Y, Yang X (2016) A comparative study of multigranulation rough sets and concept lattices via rule acquisition. Knowl-Based Syst 91:152\u2013164","journal-title":"Knowl-Based Syst"},{"issue":"3","key":"4850_CR22","doi-asserted-by":"crossref","first-page":"1821","DOI":"10.1007\/s10462-021-10053-9","volume":"55","author":"W Li","year":"2022","unstructured":"Li W, Xu W, Zhang X, Zhang J (2022) Updating approximations with dynamic objects based on local multigranulation rough sets in ordered information systems. Artif Intell Rev 55(3):1821\u20131855","journal-title":"Artif Intell Rev"},{"key":"4850_CR23","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.neucom.2018.08.065","volume":"318","author":"Y Li","year":"2018","unstructured":"Li Y, Lin Y, Liu J, Weng W, Shi Z, Wu S (2018) Feature selection for multi-label learning based on kernelized fuzzy rough sets. Neurocomput 318:271\u2013286","journal-title":"Neurocomput"},{"key":"4850_CR24","doi-asserted-by":"crossref","first-page":"109795","DOI":"10.1016\/j.knosys.2022.109795","volume":"255","author":"P Liang","year":"2022","unstructured":"Liang P, Lei D, Chin K, Hu J (2022) Feature selection based on robust fuzzy rough sets using kernel-based similarity and relative classification uncertainty measures. Knowl-Based Syst 255:109795","journal-title":"Knowl-Based Syst"},{"key":"4850_CR25","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/j.ins.2019.07.051","volume":"505","author":"K Liu","year":"2019","unstructured":"Liu K, Yang X, Fujita H, Liu D, Yang X, Qian Y (2019) An efficient selector for multi-granularity attribute reduction. Inf Sci 505:457\u2013472","journal-title":"Inf Sci"},{"key":"4850_CR26","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.ins.2020.05.076","volume":"538","author":"X Ma","year":"2020","unstructured":"Ma X (2020) Measures associated with granularity and rough approximations in interval-valued information tables based on kernel similarity relations. Inf Sci 538:337\u2013357","journal-title":"Inf Sci"},{"issue":"Dec","key":"4850_CR27","first-page":"2603","volume":"7","author":"B Moser","year":"2006","unstructured":"Moser B (2006) On representing and generating kernels by fuzzy equivalence relations. J Mach Learn Res 7(Dec):2603\u20132620","journal-title":"J Mach Learn Res"},{"issue":"5","key":"4850_CR28","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/BF01001956","volume":"11","author":"Z Pawlak","year":"1982","unstructured":"Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11(5):341\u2013356","journal-title":"Int J Comput Inf Sci"},{"key":"4850_CR29","doi-asserted-by":"crossref","DOI":"10.1201\/9781315216737","volume-title":"Granular computing: analysis and design of intelligent systems","author":"W Pedrycz","year":"2018","unstructured":"Pedrycz W (2018) Granular computing: analysis and design of intelligent systems. CRC Press"},{"issue":"6","key":"4850_CR30","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1016\/j.ins.2009.11.023","volume":"180","author":"Y Qian","year":"2010","unstructured":"Qian Y, Liang J, Yao Y, Dang C (2010) Mgrs: A multi-granulation rough set. Inf Sci 180(6):949\u2013970","journal-title":"Inf Sci"},{"key":"4850_CR31","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1016\/j.ins.2019.05.080","volume":"507","author":"B Sun","year":"2020","unstructured":"Sun B, Chen X, Zhang L, Ma W (2020) Three-way decision making approach to conflict analysis and resolution using probabilistic rough set over two universes. Inf Sci 507:809\u2013822","journal-title":"Inf Sci"},{"issue":"3","key":"4850_CR32","doi-asserted-by":"crossref","first-page":"1887","DOI":"10.1007\/s10462-021-10048-6","volume":"55","author":"B Sun","year":"2022","unstructured":"Sun B, Tong S, Ma W, Wang T, Jiang C (2022) An approach to mcgdm based on multi-granulation pythagorean fuzzy rough set over two universes and its application to medical decision problem. Artif Intell Rev 55(3):1887\u20131913","journal-title":"Artif Intell Rev"},{"issue":"5","key":"4850_CR33","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1109\/TFUZZ.2021.3053844","volume":"30","author":"L Sun","year":"2021","unstructured":"Sun L, Yin T, Ding W, Qian Y, Xu J (2021) Feature selection with missing labels using multilabel fuzzy neighborhood rough sets and maximum relevance minimum redundancy. IEEE Trans Fuzzy Syst 30(5):1197\u20131211","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"4850_CR34","doi-asserted-by":"crossref","unstructured":"Sun L, Wang T, Ding W, Xu J (2022) Partial multilabel learning using fuzzy neighbourhood-based ball clustering and kernel extreme learning machine. IEEE Transactions on Fuzzy Systems","DOI":"10.1109\/TFUZZ.2022.3222941"},{"key":"4850_CR35","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.inffus.2023.02.016","volume":"95","author":"L Sun","year":"2023","unstructured":"Sun L, Si S, Ding W, Wang X, Xu J (2023) Tfsfb: Two-stage feature selection via fusing fuzzy multi-neighborhood rough set with binary whale optimization for imbalanced data. Inf Fusion 95:91\u2013108","journal-title":"Inf Fusion"},{"key":"4850_CR36","doi-asserted-by":"crossref","first-page":"1844","DOI":"10.1007\/s12559-021-09934-6","volume":"14","author":"Q Wan","year":"2021","unstructured":"Wan Q, Li J, Wei L (2021) Optimal granule combination selection based on multi-granularity triadic concept analysis. Cognit Comput 14:1844\u20131858","journal-title":"Cognit Comput"},{"issue":"7","key":"4850_CR37","doi-asserted-by":"crossref","first-page":"2986","DOI":"10.1109\/TNNLS.2017.2712823","volume":"29","author":"C Wang","year":"2018","unstructured":"Wang C, Hu Q, Wang X, Chen D, Qian Y, Dong Z (2018) Feature selection based on neighborhood discrimination index. IEEE Trans Neural Netw Learn Syst 29(7):2986\u20132999","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4850_CR38","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.knosys.2018.10.038","volume":"164","author":"C Wang","year":"2019","unstructured":"Wang C, Huang Y, Shao M, Fan X (2019) Fuzzy rough set-based attribute reduction using distance measures. Knowl-Based Syst 164:205\u2013212","journal-title":"Knowl-Based Syst"},{"key":"4850_CR39","unstructured":"Witten IH, Frank E, Hall MA (2005) Practical machine learning tools and techniques. Morgan Kaufmann p 578"},{"key":"4850_CR40","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.inffus.2017.09.012","volume":"41","author":"J Wu","year":"2018","unstructured":"Wu J, Dai L, Chiclana F, Fujita H, Herrera-Viedma E (2018) A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust. Inf Fusion 41:232\u2013242","journal-title":"Inf Fusion"},{"key":"4850_CR41","doi-asserted-by":"crossref","first-page":"9148","DOI":"10.1007\/s10489-021-02861-x","volume":"52","author":"W Xu","year":"2022","unstructured":"Xu W, Yuan K, Li W (2022) Dynamic updating approximations of local generalized multigranulation neighborhood rough set. Appl Intell 52:9148\u20139137","journal-title":"Appl Intell"},{"issue":"3","key":"4850_CR42","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.omega.2004.04.008","volume":"33","author":"Z Xu","year":"2005","unstructured":"Xu Z (2005) Deviation measures of linguistic preference relations in group decision making. Omega 33(3):249\u2013254","journal-title":"Omega"},{"issue":"1","key":"4850_CR43","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1186\/s12916-021-01953-2","volume":"19","author":"H Yang","year":"2021","unstructured":"Yang H, Chen L, Cheng Z, Yang M, Wang J, Lin C, Wang Y, Huang L, Chen Y, Peng S, Ke Z, Li W (2021) Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study. BMC Med 19(1):80","journal-title":"BMC Med"},{"issue":"2","key":"4850_CR44","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.ijar.2007.05.019","volume":"49","author":"Y Yao","year":"2008","unstructured":"Yao Y (2008) Probabilistic rough set approximations. International journal of approximate reasoning 49(2):255\u2013271","journal-title":"International journal of approximate reasoning"},{"key":"4850_CR45","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.ins.2022.10.037","volume":"615","author":"J Ye","year":"2022","unstructured":"Ye J, Sun B, Zhan J, Chu X (2022) Variable precision multi-granulation composite rough sets with multi-decision and their applications to medical diagnosis. Inf Sci 615:293\u2013322","journal-title":"Inf Sci"},{"key":"4850_CR46","doi-asserted-by":"crossref","first-page":"107398","DOI":"10.1016\/j.knosys.2021.107398","volume":"231","author":"Z Yuan","year":"2021","unstructured":"Yuan Z, Chen H, Yang X, Li T, Liu K (2021) Fuzzy complementary entropy using hybrid-kernel function and its unsupervised attribute reduction. Knowl-Based Syst 231:107398","journal-title":"Knowl-Based Syst"},{"key":"4850_CR47","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.fss.2014.08.014","volume":"258","author":"A Zeng","year":"2015","unstructured":"Zeng A, Li T, Liu D, Zhang J, Chen H (2015) A fuzzy rough set approach for incremental feature selection on hybrid information systems. Fuzzy Sets Syst 258:39\u201360","journal-title":"Fuzzy Sets Syst"},{"key":"4850_CR48","doi-asserted-by":"publisher","unstructured":"Zhan J, Deng J, Xu Z, Mart\u00ednez L (2023) A three-way decision methodology with regret theory via triangular fuzzy numbers in incomplete multi-scale decision information systems. IEEE Transactions on Fuzzy Systems. https:\/\/doi.org\/10.1109\/TFUZZ.2023.3237646","DOI":"10.1109\/TFUZZ.2023.3237646"},{"issue":"2","key":"4850_CR49","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1109\/JAS.2022.106061","volume":"10","author":"J Zhan","year":"2023","unstructured":"Zhan J, Wang J, Ding W, Yao Y (2023) Three-way behavioral decision making with hesitant fuzzy information systems: Survey and challenges. IEEE\/CAA J Autom Sin 10(2):330\u2013350","journal-title":"IEEE\/CAA J Autom Sin"},{"key":"4850_CR50","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ins.2013.08.016","volume":"257","author":"J Zhang","year":"2014","unstructured":"Zhang J, Li T, Chen H (2014) Composite rough sets for dynamic data mining. Inf Sci 257:81\u2013100","journal-title":"Inf Sci"},{"key":"4850_CR51","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.inffus.2022.12.027","volume":"93","author":"P Zhang","year":"2023","unstructured":"Zhang P, Li T, Wang G, Wang D, Lai P, Zhang F (2023) A multi-source information fusion model for outlier detection. Inf Fusion 93:192\u2013208","journal-title":"Inf Fusion"},{"key":"4850_CR52","doi-asserted-by":"publisher","unstructured":"Zhu J, Ma X, Mart\u00ednez L, Zhan J (2023) A probabilistic linguistic three-way decision method with regret theory via fuzzy c-means clustering algorithm. IEEE Transactions on Fuzzy Systems. https:\/\/doi.org\/10.1109\/TFUZZ.2023.3236386","DOI":"10.1109\/TFUZZ.2023.3236386"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04850-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04850-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04850-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T14:10:01Z","timestamp":1698070201000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04850-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,29]]},"references-count":52,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["4850"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04850-8","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,29]]},"assertion":[{"value":"28 June 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2023","order":2,"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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}