{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:01:24Z","timestamp":1743026484595,"version":"3.40.3"},"publisher-location":"Cham","reference-count":78,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031434709"},{"type":"electronic","value":"9783031434716"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-43471-6_4","type":"book-chapter","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T10:05:42Z","timestamp":1694772342000},"page":"71-106","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A General-Purpose Multi-stage Multi-group Machine Learning Framework for Knowledge Discovery and Decision Support"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0415-4640","authenticated-orcid":false,"given":"Eva K.","family":"Lee","sequence":"first","affiliation":[]},{"given":"Fan","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Barton J.","family":"Man","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1470-5875","authenticated-orcid":false,"given":"Brent","family":"Egan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,16]]},"reference":[{"key":"4_CR1","unstructured":"Lee, E.K., Egan, B.M.: A multi-stage multi-group classification model: applications to knowledge discovery for evidence-based patient-centered care. In: Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, vol. 1, pp. 95\u2013108 (2022). KDIR. ISBN 978-989-758-614-9. ISSN 2184-3228"},{"key":"4_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-51469-7_1","volume-title":"Machine Learning, Optimization, and Big Data","author":"EK Lee","year":"2016","unstructured":"Lee, E.K., Wang, Y., Hagen, M.S., Wei, X., Davis, R.A., Egan, B.M.: Machine learning: multi-site evidence-based best practice discovery. In: Pardalos, P.M., Conca, P., Giuffrida, G., Nicosia, G. (eds.) MOD 2016. LNCS, vol. 10122, pp. 1\u201315. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-51469-7_1"},{"key":"4_CR3","doi-asserted-by":"publisher","unstructured":"Rose, S.: Machine learning for prediction in electronic health data. JAMA Netw. Open 1(4) (2018). https:\/\/doi.org\/10.1001\/jamanetworkopen.2018.1404","DOI":"10.1001\/jamanetworkopen.2018.1404"},{"key":"4_CR4","doi-asserted-by":"publisher","unstructured":"Marlin, B.M., Zemel, R.S., Roweis, S.T., Slaney, M.: Recommender systems: missing data and statistical model estimation. In: IJCAI International Joint Conference on Artificial Intelligence (2011). https:\/\/doi.org\/10.5591\/978-1-57735-516-8\/IJCAI11-447","DOI":"10.5591\/978-1-57735-516-8\/IJCAI11-447"},{"key":"4_CR5","doi-asserted-by":"publisher","unstructured":"McDermott, M.B.A., Yan, T., Naumann, T., Hunt, N., Suresh, H., Szolovits, P., Ghassemi, M.: Semi-supervised biomedical translation with cycle Wasserstein regression GaNs. In: 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (2018). https:\/\/doi.org\/10.1609\/aaai.v32i1.11890","DOI":"10.1609\/aaai.v32i1.11890"},{"key":"4_CR6","unstructured":"Mohan, K., Pearl, J., Tian, J.: Graphical models for inference with missing data. In: Advances in Neural Information Processing Systems (2013)"},{"key":"4_CR7","doi-asserted-by":"publisher","unstructured":"Rajkomar, A., Hardt, M., Howell, M.D., Corrado, G., Chin, M.H.: Ensuring fairness in machine learning to advance health equity. Ann. Internal Med. 169(12) (2018). https:\/\/doi.org\/10.7326\/M18-1990","DOI":"10.7326\/M18-1990"},{"key":"4_CR8","doi-asserted-by":"publisher","unstructured":"Lee, E.K., Wang, Y., He, Y., Egan, B.M.: An efficient, robust, and customizable information extraction and pre-processing pipeline for electronic health records. In: IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, vol. 1 (2019). https:\/\/doi.org\/10.5220\/0008071303100321","DOI":"10.5220\/0008071303100321"},{"key":"4_CR9","doi-asserted-by":"crossref","unstructured":"Lee, E.K., Egan, B.M.: Free text to standardized concepts to clinical decisions. In: Wang, J. (ed.) Encyclopedia of Data Science and Machine Learning. IGI Global (2022)","DOI":"10.4018\/978-1-7998-9220-5.ch028"},{"key":"4_CR10","unstructured":"Lee, E.K., Yuan, F., Hirsh, D.A., Mallory, M.D., Simon, H.K.: A clinical decision tool for predicting patient care characteristics: patients returning within 72 hours in the emergency department. In: AMIA Annual Symposium Proceedings\/AMIA Symposium. AMIA Symposium 2012 (2012)"},{"key":"4_CR11","unstructured":"Suresh, H., et al.: Proceedings of Machine Learning for Healthcare 2017 Clinical Intervention Prediction and Understanding with Deep Neural Networks. Ml4H, 68 (2017)"},{"key":"4_CR12","doi-asserted-by":"publisher","unstructured":"Basha, S.J., Madala, S.R., Vivek, K., Kumar, E.S., Ammannamma, T.: A review on imbalanced data classification techniques. In: 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA), pp. 1\u20136 (2022). https:\/\/doi.org\/10.1109\/ICACTA54488.2022.9753392","DOI":"10.1109\/ICACTA54488.2022.9753392"},{"key":"4_CR13","doi-asserted-by":"publisher","first-page":"178","DOI":"10.3389\/fpubh.2020.00178","volume":"8","author":"K Fujiwara","year":"2020","unstructured":"Fujiwara, K., et al.: Over- and under-sampling approach for extremely imbalanced and small minority data problem in health record analysis. Front. Public Health 8, 178 (2020). https:\/\/doi.org\/10.3389\/fpubh.2020.00178","journal-title":"Front. Public Health"},{"key":"4_CR14","doi-asserted-by":"publisher","unstructured":"Gao, L., Zhang, L., Liu, C., Wu, S.: Handling imbalanced medical image data: a deep-learning-based one-class classification approach. Artif. Intell. Med. 108 (2020). https:\/\/doi.org\/10.1016\/j.artmed.2020.101935","DOI":"10.1016\/j.artmed.2020.101935"},{"key":"4_CR15","unstructured":"O\u2019Leary, L.: How IBM\u2019s Watson Went From the Future of Health Care to Sold Off for Parts. https:\/\/slate.com\/technology\/2022\/01\/ibm-watson-health-failure-artificial-intelligence.html. Accessed 22 Jan 2023"},{"key":"4_CR16","unstructured":"Sweeney, E.: Experts say IBM Watson\u2019s flaws are rooted in data collection and interoperability. https:\/\/www.fiercehealthcare.com\/analytics\/ibm-watson-s-flaws-trace-back-to-data-collection-interoperability. Accessed 23 Jan 2023"},{"key":"4_CR17","doi-asserted-by":"publisher","unstructured":"Lee, E.K., Li, Z., Wang, Y., Hagen, M.S., Davis, R., Egan, B.M.: Multi-site best practice discovery: from free text to standardized concepts to clinical decisions. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2766\u20132773 (2021). https:\/\/doi.org\/10.1109\/BIBM52615.2021.9669414","DOI":"10.1109\/BIBM52615.2021.9669414"},{"key":"4_CR18","unstructured":"Ghassemi, M., Naumann, T., Schulam, P., Beam, A.L., Chen, I.Y., Ranganath, R.: A review of challenges and opportunities in machine learning for health. In: AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science, 2020 (2020)"},{"key":"4_CR19","doi-asserted-by":"publisher","unstructured":"Cui, L., Yang, S., Chen, F., Ming, Z., Lu, N., Qin, J.: A survey on application of machine learning for Internet of Things. Int. J. Mach. Learn. Cybern. 9(8) (2018). https:\/\/doi.org\/10.1007\/s13042-018-0834-5","DOI":"10.1007\/s13042-018-0834-5"},{"key":"4_CR20","doi-asserted-by":"publisher","unstructured":"Dixon, M.F., Halperin, I., Bilokon, P.: Machine learning in finance: from theory to practice. In: Machine Learning in Finance: From Theory to Practice (2020). https:\/\/doi.org\/10.1007\/978-3-030-41068-1","DOI":"10.1007\/978-3-030-41068-1"},{"key":"4_CR21","doi-asserted-by":"publisher","unstructured":"Hayward, K.J., Maas, M.M.: Artificial intelligence and crime: a primer for criminologists. Crime Media Cult. 17(2) (2021). https:\/\/doi.org\/10.1177\/1741659020917434","DOI":"10.1177\/1741659020917434"},{"key":"4_CR22","doi-asserted-by":"publisher","unstructured":"Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., Nandi, A.K.: Applications of machine learning to machine fault diagnosis: a review and roadmap. Mech. Syst. Signal Process. 138 (2020). https:\/\/doi.org\/10.1016\/j.ymssp.2019.106587","DOI":"10.1016\/j.ymssp.2019.106587"},{"key":"4_CR23","doi-asserted-by":"publisher","unstructured":"Myszczynska, M.A., et al.: Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat. Rev. Neurol. 16(8) (2020). https:\/\/doi.org\/10.1038\/s41582-020-0377-8","DOI":"10.1038\/s41582-020-0377-8"},{"key":"4_CR24","doi-asserted-by":"publisher","unstructured":"Narciso, D.A.C., Martins, F.G.: Application of machine learning tools for energy efficiency in industry: a review. Energy Rep. 6 (2020). https:\/\/doi.org\/10.1016\/j.egyr.2020.04.035","DOI":"10.1016\/j.egyr.2020.04.035"},{"key":"4_CR25","doi-asserted-by":"publisher","unstructured":"Qu, K., Guo, F., Liu, X., Lin, Y., Zou, Q.: Application of machine learning in microbiology. Front. Microbiol. 10(Apr) (2019). https:\/\/doi.org\/10.3389\/fmicb.2019.00827","DOI":"10.3389\/fmicb.2019.00827"},{"key":"4_CR26","doi-asserted-by":"publisher","unstructured":"Yarkoni, T., Westfall, J.: Choosing prediction over explanation in psychology: lessons from machine learning. Perspect. Psychol. Sci. 12(6) (2017). https:\/\/doi.org\/10.1177\/1745691617693393","DOI":"10.1177\/1745691617693393"},{"key":"4_CR27","doi-asserted-by":"publisher","unstructured":"Zhao, S., et al.: Application of machine learning in intelligent fish aquaculture: a review. Aquaculture 540 (2021). https:\/\/doi.org\/10.1016\/j.aquaculture.2021.736724","DOI":"10.1016\/j.aquaculture.2021.736724"},{"key":"4_CR28","doi-asserted-by":"publisher","unstructured":"Efron, B., et al.: Least angle regression. Ann. Stat. 32(2) (2004). https:\/\/doi.org\/10.1214\/009053604000000067","DOI":"10.1214\/009053604000000067"},{"key":"4_CR29","doi-asserted-by":"publisher","unstructured":"Tibshirani, R.: Regression shrinkage and selection via the lasso: a retrospective. J. Roy. Stat. Soc. Ser. B Stat. Methodol. 73(3) (2011). https:\/\/doi.org\/10.1111\/j.1467-9868.2011.00771.x","DOI":"10.1111\/j.1467-9868.2011.00771.x"},{"key":"4_CR30","doi-asserted-by":"publisher","unstructured":"Hocking, R.R., Leslie, R.N.: Selection of the best subset in regression analysis. Technometrics 9(4) (1967). https:\/\/doi.org\/10.1080\/00401706.1967.10490502","DOI":"10.1080\/00401706.1967.10490502"},{"key":"4_CR31","doi-asserted-by":"publisher","unstructured":"Pudil, P., Novovi\u010dov\u00e1, J., Kittler, J.: Floating search methods in feature selection. Pattern Recognit. Lett. 15(11) (1994). https:\/\/doi.org\/10.1016\/0167-8655(94)90127-9","DOI":"10.1016\/0167-8655(94)90127-9"},{"key":"4_CR32","doi-asserted-by":"publisher","unstructured":"Silva, A.P.D., Stam, A.: Second order mathematical programming formulations for discriminant analysis. Eur. J. Oper. Res. 72(1) (1994). https:\/\/doi.org\/10.1016\/0377-2217(94)90324-7","DOI":"10.1016\/0377-2217(94)90324-7"},{"key":"4_CR33","doi-asserted-by":"publisher","unstructured":"Siedlecki, W., Sklansky, J.: A note on genetic algorithms for large-scale feature selection. Pattern Recognit. Lett. 10(5) (1989). https:\/\/doi.org\/10.1016\/0167-8655(89)90037-8","DOI":"10.1016\/0167-8655(89)90037-8"},{"key":"4_CR34","doi-asserted-by":"publisher","unstructured":"Kennedy, J., Eberhart, R.C.: Discrete binary version of the particle swarm algorithm. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, vol. 5 (1997). https:\/\/doi.org\/10.1109\/icsmc.1997.637339","DOI":"10.1109\/icsmc.1997.637339"},{"key":"4_CR35","doi-asserted-by":"publisher","unstructured":"Agrafiotis, D.K., Cede\u00f1o, W.: Feature selection for structure-activity correlation using binary particle swarms. J. Med. Chem. 45(5) (2002). https:\/\/doi.org\/10.1021\/jm0104668","DOI":"10.1021\/jm0104668"},{"key":"4_CR36","doi-asserted-by":"publisher","unstructured":"Correa, E.S., Freitas, A.A., Johnson, C.G.: A new discrete particle swarm algorithm applied to attribute selection in a bioinformatics data set. In: GECCO 2006 - Genetic and Evolutionary Computation Conference, vol. 1 (2006). https:\/\/doi.org\/10.1145\/1143997.1144003","DOI":"10.1145\/1143997.1144003"},{"key":"4_CR37","doi-asserted-by":"publisher","unstructured":"Hu, Y., Zhang, Y., Gong, D.: Multiobjective particle swarm optimization for feature selection with fuzzy cost. IEEE Trans. Cybern. 51(2) (2021). https:\/\/doi.org\/10.1109\/TCYB.2020.3015756","DOI":"10.1109\/TCYB.2020.3015756"},{"key":"4_CR38","doi-asserted-by":"publisher","unstructured":"Jain, N.K., Nangia, U., Jain, J.: A review of particle swarm optimization. J. Inst. Eng. (India): Ser. B 99(4) (2018). https:\/\/doi.org\/10.1007\/s40031-018-0323-y","DOI":"10.1007\/s40031-018-0323-y"},{"key":"4_CR39","doi-asserted-by":"publisher","unstructured":"Monteiro, S.T., Kosugi, Y.: Particle swarms for feature extraction of hyperspectral data. IEICE Trans. Inf. Syst. E90-D(7) (2007). https:\/\/doi.org\/10.1093\/ietisy\/e90-d.7.1038","DOI":"10.1093\/ietisy\/e90-d.7.1038"},{"key":"4_CR40","doi-asserted-by":"publisher","unstructured":"Gallagher, R.J., Lee, E.K., Patterson, D.A.: Constrained discriminant analysis via 0\/1 mixed integer programming. Ann. Oper. Res. 74 (1997). https:\/\/doi.org\/10.1023\/a:1018943025993","DOI":"10.1023\/a:1018943025993"},{"key":"4_CR41","unstructured":"World Health Organization. Cardiovascular diseases (2022). https:\/\/www.who.int\/health-topics\/cardiovascular-diseases#tab=tab_1. Accessed 23 Jan 2023"},{"key":"4_CR42","doi-asserted-by":"publisher","unstructured":"Tsao, C.W., et al.: Heart disease and stroke statistics-2022 update: a report from the American heart association. Circulation 145(8), e153\u2013e639 (2022). https:\/\/doi.org\/10.1161\/CIR.0000000000001052. Epub 2022 Jan 26. Erratum in: Circulation. 2022 Sep 6;146(10):e141. PMID: 35078371","DOI":"10.1161\/CIR.0000000000001052"},{"key":"4_CR43","unstructured":"Cardiovascular diseases affect nearly half of American adults, statistics show. American Heart Association News (2019). https:\/\/www.heart.org\/en\/news\/2019\/01\/31\/cardiovascular-diseases-affect-nearly-half-of-american-adults-statistics-show"},{"key":"4_CR44","doi-asserted-by":"publisher","unstructured":"Gordon, T., Castelli, W.P., Hjortland, M.C., Kannel, W.B., Dawber, T.R.: High density lipoprotein as a protective factor against coronary heart disease. The Framingham study. Am. J. Med. 62(5) (1977). https:\/\/doi.org\/10.1016\/0002-9343(77)90874-9","DOI":"10.1016\/0002-9343(77)90874-9"},{"key":"4_CR45","doi-asserted-by":"publisher","unstructured":"Nwegbu, N., Tirunagari, S., Windridge, D.: A novel kernel based approach to arbitrary length symbolic data with application to type 2 diabetes risk. Sci. Rep. 12(1) (2022). https:\/\/doi.org\/10.1038\/s41598-022-08757-1","DOI":"10.1038\/s41598-022-08757-1"},{"key":"4_CR46","doi-asserted-by":"crossref","unstructured":"Ogurtsova, K., et al.: IDF diabetes atlas: global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res. Clin. Pract. 128 (2017)","DOI":"10.1016\/j.diabres.2017.03.024"},{"key":"4_CR47","doi-asserted-by":"publisher","first-page":"929","DOI":"10.2337\/dci18-0012","volume":"41","author":"MC Riddle","year":"2018","unstructured":"Riddle, M.C., Herman, W.H.: The cost of diabetes care\u2014an elephant in the room. Diabetes Care 41, 929\u2013932 (2018)","journal-title":"Diabetes Care"},{"key":"4_CR48","unstructured":"American Diabetes Association. Statistics About Diabetes (2022). https:\/\/diabetes.org\/about-us\/statistics\/about-diabetes"},{"key":"4_CR49","doi-asserted-by":"publisher","unstructured":"American Diabetes Association. Economic Costs of Diabetes in the U.S. in 2017. Diabetes Care 41(5), 917\u2013928 (2018). https:\/\/doi.org\/10.2337\/dci18-0007. PMID 29567642; PMCID PMC5911784","DOI":"10.2337\/dci18-0007"},{"key":"4_CR50","doi-asserted-by":"publisher","unstructured":"Nathan, D.M., et al.: Diabetes control and complications trial\/epidemiology of diabetes interventions and complications (DCCT\/EDIC) study research group. Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. N. Engl. J. Med. 353(25), 2643\u20132653 (2005). https:\/\/doi.org\/10.1056\/NEJMoa052187. PMID 16371630; PMCID PMC2637991","DOI":"10.1056\/NEJMoa052187"},{"key":"4_CR51","doi-asserted-by":"crossref","unstructured":"Caiado, J., Crato, N., Pe\u00f1a, D.: Comparison of times series with unequal length in the frequency domain. Commun. Stat. Simul. Comput.\u00ae 38(3), 527\u2013540 (2009)","DOI":"10.1080\/03610910802562716"},{"key":"4_CR52","unstructured":"World Health Organization. The top 10 causes of death (2022). https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/the-top-10-causes-of-death. Accessed 24 Jan 2023"},{"key":"4_CR53","doi-asserted-by":"publisher","unstructured":"Kluger, A., Ferris, S.H., Golomb, J., Mittelman, M.S., Reisberg, B.: Neuropsychological prediction of decline to dementia in nondemented elderly. J. Geriatr. Psychiatry Neurol. 12(4) (1999). https:\/\/doi.org\/10.1177\/089198879901200402","DOI":"10.1177\/089198879901200402"},{"key":"4_CR54","doi-asserted-by":"publisher","unstructured":"Lopez, O.L., et al.: Neuropsychological characteristics of mild cognitive impairment subgroups. J. Neurol. Neurosurg. Psychiatry 77(2) (2006). https:\/\/doi.org\/10.1136\/jnnp.2004.045567","DOI":"10.1136\/jnnp.2004.045567"},{"key":"4_CR55","doi-asserted-by":"publisher","unstructured":"Lee, E.K., Wu, T.L.: Classification and disease prediction via mathematical programming. In: Springer Optimization and Its Applications, vol. 26 (2009). https:\/\/doi.org\/10.1007\/978-0-387-09770-1_12","DOI":"10.1007\/978-0-387-09770-1_12"},{"key":"4_CR56","doi-asserted-by":"publisher","unstructured":"Lee, E.K., Wu, T.L., Goldstein, F., Levey, A.: Predictive model for early detection of mild cognitive impairment and Alzheimer\u2019s disease. Fields Inst. Commun. 63 (2012). https:\/\/doi.org\/10.1007\/978-1-4614-4133-5_4","DOI":"10.1007\/978-1-4614-4133-5_4"},{"key":"4_CR57","doi-asserted-by":"publisher","unstructured":"Stuss, D.T., Trites, R.L.: Classification of neurological status using multiple discriminant function analysis of neuropsychological test scores. J. Consult. Clin. Psychol. 45(1) (1977). https:\/\/doi.org\/10.1037\/0022-006X.45.1.145","DOI":"10.1037\/0022-006X.45.1.145"},{"key":"4_CR58","doi-asserted-by":"publisher","unstructured":"Tabert, M.H., et al.: Neuropsychological prediction of conversion to Alzheimer disease in patients with mild cognitive impairment. Arch. Gen. Psychiatry 63(8) (2006). https:\/\/doi.org\/10.1001\/archpsyc.63.8.916","DOI":"10.1001\/archpsyc.63.8.916"},{"key":"4_CR59","doi-asserted-by":"publisher","unstructured":"Hu, W.T., et al.: Plasma multianalyte profiling in mild cognitive impairment and Alzheimer Disease. Neurology 79(9) (2012). https:\/\/doi.org\/10.1212\/WNL.0b013e318266fa70","DOI":"10.1212\/WNL.0b013e318266fa70"},{"key":"4_CR60","doi-asserted-by":"publisher","unstructured":"Hu, W.T., et al.: CSF complement 3 and factor H are staging biomarkers in Alzheimer\u2019s disease. Acta Neuropathol. Commun. 4 (2016). https:\/\/doi.org\/10.1186\/s40478-016-0277-8","DOI":"10.1186\/s40478-016-0277-8"},{"key":"4_CR61","doi-asserted-by":"publisher","unstructured":"Palmqvist, S., et al.: Discriminative accuracy of plasma phospho-tau217 for Alzheimer disease vs other neurodegenerative disorders. JAMA J. Am. Med. Assoc. 324(8) (2020). https:\/\/doi.org\/10.1001\/jama.2020.12134","DOI":"10.1001\/jama.2020.12134"},{"key":"4_CR62","doi-asserted-by":"publisher","unstructured":"Ray, S., et al.: Classification and prediction of clinical Alzheimer\u2019s diagnosis based on plasma signaling proteins. Nat. Med. 13(11) (2007). https:\/\/doi.org\/10.1038\/nm1653","DOI":"10.1038\/nm1653"},{"key":"4_CR63","doi-asserted-by":"publisher","unstructured":"Reddy, M.M., et al.: Identification of candidate IgG biomarkers for Alzheimer\u2019s disease via combinatorial library screening. Cell 144(1) (2011). https:\/\/doi.org\/10.1016\/j.cell.2010.11.054","DOI":"10.1016\/j.cell.2010.11.054"},{"key":"4_CR64","doi-asserted-by":"publisher","unstructured":"Rocha de Paula, M.R., G\u00f3mez Ravetti, M., Berretta, R., Moscato, P.: Differences in abundances of cell-signalling proteins in blood reveal novel biomarkers for early detection of clinical Alzheimer\u2019s disease. PLoS ONE 6(3) (2011). https:\/\/doi.org\/10.1371\/journal.pone.0017481","DOI":"10.1371\/journal.pone.0017481"},{"key":"4_CR65","doi-asserted-by":"publisher","unstructured":"Schindler, S.E., Bateman, R.J.: Combining blood-based biomarkers to predict risk for Alzheimer\u2019s disease dementia. Nat. Aging 1(1) (2021). https:\/\/doi.org\/10.1038\/s43587-020-00008-0","DOI":"10.1038\/s43587-020-00008-0"},{"issue":"8","key":"4_CR66","doi-asserted-by":"publisher","first-page":"2134","DOI":"10.1002\/art.38685","volume":"66","author":"DL Riddle","year":"2014","unstructured":"Riddle, D.L., Jiranek, W.A., Hayes, C.W.: Use of a validated algorithm to judge the appropriateness of total knee arthroplasty in the united states: a multicenter longitudinal cohort study. Arthritis Rheumatol. 66(8), 2134\u20132143 (2014)","journal-title":"Arthritis Rheumatol."},{"key":"4_CR67","doi-asserted-by":"publisher","unstructured":"Mora, J.C., Przkora, R., Cruz-Almeida, Y.: Knee osteoarthritis: pathophysiology and current treatment modalities. J. Pain Res. 11, 2189\u20132196 (2018). https:\/\/doi.org\/10.2147\/JPR.S154002. PMID: 30323653; PMCID: PMC6179584.","DOI":"10.2147\/JPR.S154002"},{"key":"4_CR68","unstructured":"Bellamy, N.: WOMAC Osteoarthritis Index User Guide. Version V. Brisbane, Australia (2002)"},{"key":"4_CR69","doi-asserted-by":"crossref","unstructured":"Hays, R.D., Sherbourne, C.D., Mazel, R.M.: The RAND 36-item health survey 1.0. Health Econ. 2(3), 217\u2013227 (1993)","DOI":"10.1002\/hec.4730020305"},{"key":"4_CR70","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1177\/03635465010290021601","volume":"29","author":"RG Marx","year":"2001","unstructured":"Marx, R.G., Stump, T.J., Jones, E.C., Wickiewicz, T.L., Warren, R.F.: Development and evaluation of an activity rating scale for disorders of the knee. Am. J. Sports Med. 29, 213\u2013218 (2001)","journal-title":"Am. J. Sports Med."},{"key":"4_CR71","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1002\/art.10993","volume":"49","author":"O Sangha","year":"2003","unstructured":"Sangha, O., Stucki, G., Liang, M.H., Fossel, A.H., Katz, J.N.: The self-administered comorbidity questionnaire: a new method to assess comorbidity for clinical and health services research. Arthritis Rheum. 49, 156\u2013163 (2003)","journal-title":"Arthritis Rheum."},{"issue":"1","key":"4_CR72","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0168-8510(96)00822-6","volume":"37","author":"R Brooks","year":"1996","unstructured":"Brooks, R.: EuroQol: the current state of play. Health Policy 37(1), 53\u201372 (1996)","journal-title":"Health Policy"},{"key":"4_CR73","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1002\/anr.1780320107","volume":"32","author":"K Lorig","year":"1989","unstructured":"Lorig, K., Chastain, R.L., Ung, E., Shoor, S., Holman, H.R.: Development and evaluation of a scale to measure perceived self-efficacy in people with arthritis. Arthritis Rheum. 32, 37\u201344 (1989)","journal-title":"Arthritis Rheum."},{"key":"4_CR74","unstructured":"Ebrahimzadeh, M.H., Makhmalbaf, H., Birjandinejad, A., Keshtan, F.G., Hoseini, H.A., Mazloumi, S.M.: The western Ontario and Mcmaster universities osteoarthritis index (WOMAC) in Persian speaking patients with knee osteoarthritis. Arch. Bone Jt. Surg. 2(1), 57\u201362 (2014). PMID 25207315; PMCID PMC4151432"},{"key":"4_CR75","first-page":"792","volume":"24","author":"MC Hochberg","year":"1997","unstructured":"Hochberg, M.C., Altman, R.D., Brandt, K.D., Moskowitz, R.W.: Design and conduct of clinical trials in osteoarthritis: preliminary recommendations from a task force of the osteoarthritis research society. J. Rheumatol. 24, 792\u2013794 (1997)","journal-title":"J. Rheumatol."},{"key":"4_CR76","unstructured":"Lee, E.K., Mann, B.J., DeMaio, M.: Prediction of responses to intra-articular injections of Hyaluronic acid for knee osteoarthritis. Preprint (2023)"},{"key":"4_CR77","doi-asserted-by":"publisher","unstructured":"Lee, E.K., Gallagher, R.J., Patterson, D.A.: A linear programming approach to discriminant analysis with a reserved-judgment region. INFORMS J. Comput. 15(1) (2003). https:\/\/doi.org\/10.1287\/ijoc.15.1.23.15158","DOI":"10.1287\/ijoc.15.1.23.15158"},{"key":"4_CR78","doi-asserted-by":"publisher","unstructured":"Shapoval, A., Lee, E.K.: Generalizing 0\u20131 conflict hypergraphs and mixed conflict graphs: mixed conflict hypergraphs in discrete optimization. J. Glob. Optim. 80(4) (2021). https:\/\/doi.org\/10.1007\/s10898-021-01012-3","DOI":"10.1007\/s10898-021-01012-3"}],"container-title":["Communications in Computer and Information Science","Knowledge Discovery, Knowledge Engineering and Knowledge Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43471-6_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T10:09:26Z","timestamp":1694772566000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43471-6_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031434709","9783031434716"],"references-count":78,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43471-6_4","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"16 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IC3K","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Valletta","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malta","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ic3k2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ic3k.scitevents.org\/?y=2022","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes, PRIMORIS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"127","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"44","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"23% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"IN MS packgae : 14 full papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}