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               Japan Epidemiology Collaboration on Occupational Health Study[J].   validation of a deep learning based diabetes prediction system using
               PLoS One,2015,10(11):e0142779.                       a nationwide population-based cohort[J]. Diabetes Metab J,
           [23]WANG A,CHEN G,SU Z,et al. Risk scores for predicting incidence   2021,45(4):515-525. DOI:10.4093/dmj.2020.0081.
               of type 2 diabetes in the Chinese population:the Kailuan prospective   [40]NG K,SUN J,HU J,et al. Personalized predictive modeling
               study[J]. Sci Rep,2016,6:26548. DOI:10.1038/srep26548.  and risk factor identification using patient similarity[J]. AMIA Jt
           [24]LIU X,FINE J P,CHEN Z,et al. Prediction of the 20-year   Summits Transl Sci Proc,2015,2015:132-136.
               incidence of diabetes in older Chinese:application of the competing   [41]COWLEY  L  E,FAREWELL  D  M,MAGUIRE  S,et  al.
               risk method in a longitudinal study[J]. Medicine(Baltimore),  Methodological standards for the development and evaluation of
               2016,95(40):e5057. DOI:10.1097/MD.0000000000005057.  clinical prediction rules:a review of the literature[J]. Diagn
           [25]MIYAKOSHI T,OKA R,NAKASONE Y,et al. Development of   Progn Res,2019,3:16. DOI:10.1186/s41512-019-0060-y.
               new diabetes risk scores on the basis of the current definition of diabetes   [42]BRILLEMAN S L,CROWTHER M J,MORENO-BETANCUR M,
               in Japanese subjects[J]. Endocr J,2016,63(9):857-865.  et al. Joint longitudinal and time-to-event models for multilevel
           [26]ZHANG M,ZHANG H,WANG C,et al. Development and        hierarchical data[J]. Stat Methods Med Res,2019,28(12):
               validation of a risk-score model for type 2 diabetes:a cohort study   3502-3515. DOI:10.1177/0962280218808821.
               of a rural adult Chinese population[J]. PLoS One,2016,11(4):  [43]HACKETT R A,STEPTOE A. Type 2 diabetes mellitus and psychological
               e0152054. DOI:10.1371/journal.pone.0152054.          stress:a modifiable risk factor[J]. Nat Rev Endocrinol,2017,13(9):
           [27]CHEN X,WU Z,CHEN Y,et al. Risk score model of type 2   547-560. DOI:10.1038/nrendo.2017.64.
               diabetes prediction for rural Chinese adults:the Rural Deqing Cohort   [44]HOSSEINI  Z,WHITING  S  J,VATANPARAST  H.  Type  2
               Study[J]. J Endocrinol Invest,2017,40(10):1115-1123.  diabetes prevalence among Canadian adults:dietary habits and
           [28]ZHANG H,WANG C,REN Y,et al. A risk-score model for   sociodemographic risk factors[J]. Appl Physiol Nutr Metab,
               predicting risk of type 2 diabetes mellitus in a rural Chinese adult   2019,44(10):1099-1104. DOI:10.1139/apnm-2018-0567.
               population:a cohort study with a 6-year follow-up[J]. Diabetes   [45]KAUTZKY-WILLER A,HARREITER J,PACINI G. Sex and
               Metab Res Rev,2017,33(7):e2911. DOI:10.1002/dmrr.2911.  gender differences in risk,pathophysiology and complications of type 2
           [29]WEN J,HAO J,LIANG Y,et al. A non-invasive risk score for   diabetes mellitus[J]. Endocr Rev,2016,37(3):278-316.
               predicting incident diabetes among rural Chinese people:a village-  [46]ESTEGHAMATI A,ETEMAD K,KOOHPAYEHZADEH J,et al.
               based cohort study[J]. PLoS One,2017,12(11):e0186172.   Trends in the prevalence of diabetes and impaired fasting glucose in
           [30]YATSUYA H,LI Y,HIRAKAWA Y,et al. A point system for   association with obesity in Iran:2005—2011[J]. Diabetes Res Clin
               predicting 10-year risk of developing type 2 diabetes mellitus in   Pract,2014,103(2):319-327. DOI:10.1016/j.diabres.2013.12.034.
               Japanese men:Aichi Workers' Cohort Study[J]. J Epidemiol,  [47]MACCALLUM R C,ZHANG S,PREACHER K J,et al. On the
               2018,28(8):347-352. DOI:10.2188/jea.JE20170048.      practice of dichotomization of quantitative variables[J]. Psychol
           [31]HA K H,LEE Y H,SONG S O,et al. Development and validation   Methods,2002,7(1):19-40. DOI:10.1037/1082-989x.7.1.19.
               of the Korean Diabetes Risk Score:a 10-year national cohort study  [48]ROYSTON P,SAUERBREI W. Multivariable model-building:
               [J]. Diabetes Metab J,2018,42(5):402-414.            a pragmatic approach to regression analysis based on fractional
           [32]HAN X,WANG J,LI Y,et al. Development of a new scoring   polynomials for modelling continuous variables[M]. Chichester:
               system to predict 5-year incident diabetes risk in middle-aged and   John Wiley & Sons,2008.
               older Chinese[J]. Acta Diabetol,2018,55(1):13-19.   [49]VUONG K,MCGEECHAN K,ARMSTRONG B K,et al. Risk
           [33]HU H,NAKAGAWA T,YAMAMOTO S,et al. Development and    prediction models for incident primary cutaneous melanoma:a
               validation of risk models to predict the 7-year risk of type 2 diabetes:  systematic review[J]. JAMA Dermatol,2014,150(4):434-
               the Japan Epidemiology Collaboration on Occupational Health Study[J].   444. DOI:10.1001/jamadermatol.2013.8890.
               J Diabetes Investig,2018,9(5):1052-1059.        [50]ZHANG Q,WANG L. Moderation analysis with missing data in the
           [34]WANG K,GONG M,XIE S,et al. Nomogram prediction for the   predictors[J]. Psychol Methods,2017,22(4):649-666.
               3-year risk of type 2 diabetes in healthy mainland China residents[J].   [51]STEYERBERG E W. Clinical prediction models:a practical
               EPMA Journal,2019,10(3):227-237.                     approach to development,validation,and updating[M]. New
           [35]GUNTHER S H,KHOO C M,TAI E S,et al. Serum acylcarnitines   York:Springer,2009.
               and amino acids and risk of type 2 diabetes in a multiethnic Asian   [52]STEYERBERG E W,HARRELL F E J R,BORSBOOM G J,et al.
               population[J]. BMJ Open Diabetes Res Care,2020,8(1):  Internal validation of predictive models:efficiency of some procedures
               e001315. DOI:10.1136/bmjdrc-2020-001315.             for Logistic regression analysis[J]. J Clin Epidemiol,2001,54(8):
           [36]SHAO  X,WANG  Y,HUANG  S,et  al.  Development  and   774-781. DOI:10.1016/s0895-4356(01)00341-9.
               validation of a prediction model estimating the 10-year risk for type 2   [53]AUSTIN P C,STEYERBERG E W. Events per variable(EPV)and
               diabetes in China[J]. PLoS One,2020,15(9 ):e0237936.   the relative performance of different strategies for estimating the out-of-
           [37]ASGARI S,KHALILI D,ZAYERI F,et al. Dynamic prediction   sample validity of Logistic regression models[J]. Stat Methods Med
               models improved the risk classification of type 2 diabetes compared with   Res,2017,26(2):796-808. DOI:10.1177/0962280214558972.
               classical static models[J]. J Clin Epidemiol,2021,140:33-43.  [54]CASTALDI P J,DAHABREH I J,IOANNIDIS J P. An empirical
           [38]OH T J,MOON J H,CHOI S H,et al. Development of a clinical   assessment of validation practices for molecular classifiers[J]. Brief
               risk score for incident diabetes:a 10-year prospective cohort study  Bioinform,2011,12(3):189-202. DOI:10.1093/bib/bbq073.
               [J]. J Diabetes Investig,2021,12(4):610-618.               (收稿日期:2022-05-16;修回日期:2022-09-11)
           [39]RHEE  S  Y,SUNG  J  M,KIM  S,et  al.  Development  and                          (本文编辑:陈俊杉)
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