[1] |
WALKER A L, DE ROOIJ S R, DIMITROVA M V,et al. Psychosocial and peripartum determinants of postpartum depression:findings from a prospective population-based cohort. The ABCD study[J]. Compr Psychiatry, 2021, 108:152239. DOI: 10.1016/j.comppsych.2021.152239.
|
[2] |
QI W J, ZHAO F Q, LIU Y T,et al. Psychosocial risk factors for postpartum depression in Chinese women:a meta-analysis[J]. BMC Pregnancy Childbirth, 2021, 21(1):174. DOI: 10.1186/s12884-021-03657-0.
|
[3] |
PAYNE J L, MAGUIRE J. Pathophysiological mechanisms implicated in postpartum depression[J]. Front Neuroendocrinol, 2019, 52:165-180. DOI: 10.1016/j.yfrne.2018.12.001.
|
[4] |
YIM I S, TANNER STAPLETON L R, GUARDINO C M,et al. Biological and psychosocial predictors of postpartum depression:systematic review and call for integration[J]. Annu Rev Clin Psychol, 2015, 11:99-137. DOI: 10.1146/annurev-clinpsy-101414-020426.
|
[5] |
STEYERBERG E W. Clinical prediction models [M]. New York:Springer,2019:1-15.
|
[6] |
MOONS K G M, WOLFF R F, RILEY R D,et al. PROBAST:a tool to assess risk of bias and applicability of prediction model studies:explanation and elaboration[J]. Ann Intern Med, 2019, 170(1):W1-33. DOI: 10.7326/M18-1377.
|
[7] |
VAN SMEDEN M, REITSMA J B, RILEY R D,et al. Clinical prediction models:diagnosis versus prognosis[J]. J Clin Epidemiol, 2021, 132:142-145. DOI: 10.1016/j.jclinepi.2021.01.009.
|
[8] |
OBROCHTA C A, CHAMBERS C, BANDOLI G. Psychological distress in pregnancy and postpartum[J]. Women Birth, 2020, 33(6):583-591. DOI: 10.1016/j.wombi.2020.01.009.
|
[9] |
PITT B. "Atypical" depression following childbirth[J]. Br J Psychiatry, 1968, 114(516):1325-1335. DOI: 10.1192/bjp.114.516.1325.
|
[10] |
COOPER J. Diagnostic and Statistical Manual of Mental Disorders (4th edn,text revision) (DSM-Ⅳ-TR) Washington,DC:American Psychiatric Association 2000.943 pp. £39.99 (hb). ISBN 0 89042 025 4[J]. Br J Psychiatry, 2001, 179(1):85. DOI: 10.1192/bjp.179.1.85-a.
|
[11] |
American Psychiatric Association. Diagnostic and statistical manual of mental disorders[M]. 5th ed. Arlinton:American Psychiatric Association,2013:591-643
|
[12] |
HAHN-HOLBROOK J, CORNWELL-HINRICHS T, ANAYA I. Economic and health predictors of national postpartum depression prevalence:a systematic review,meta-analysis,and meta-regression of 291 studies from 56 countries[J]. Front Psychiatry, 2017, 8:248. DOI: 10.3389/fpsyt.2017.00248.
|
[13] |
TOMFOHR-MADSEN L M, RACINE N, GIESBRECHT G F,et al. Depression and anxiety in pregnancy during COVID-19:a rapid review and meta-analysis[J]. Psychiatry Res, 2021, 300:113912. DOI: 10.1016/j.psychres.2021.113912.
|
[14] |
GELAYE B, RONDON M B, ARAYA R,et al. Epidemiology of maternal depression,risk factors,and child outcomes in low-income and middle-income countries[J]. Lancet Psychiatry, 2016, 3(10):973-982. DOI: 10.1016/S2215-0366(16)30284-X.
|
[15] |
NISAR A, YIN J, WAQAS A,et al. Prevalence of perinatal depression and its determinants in Mainland China:a systematic review and meta-analysis[J]. J Affect Disord, 2020, 277:1022-1037. DOI: 10.1016/j.jad.2020.07.046.
|
[16] |
MU T Y, LI Y H, PAN H F,et al. Postpartum depressive mood (PDM) among Chinese women:a meta-analysis[J]. Arch Womens Ment Health, 2019, 22(2):279-287. DOI: 10.1007/s00737-018-0885-3.
|
[17] |
WANG Y Y, KONG F, QIAO J. Gender equity,caregiving,and the 1-2-3-child policy in China——authors' reply[J]. Lancet, 2021, 398(10304):953-954. DOI: 10.1016/S0140-6736(21)01749-9.
|
[18] |
STEYERBERG E W. Statistical Models for Prediction[M]// STEYERBERG E W. Clinical Prediction Models:A Practical Approach to Development,Validation,and Updating.2nd Ed. Cham:Springer International Publishing. 2019:59-93. DOI: 10.1007/978-3-030-16399-0_4.
|
[19] |
HASTIE T, TIBSHIRANI R, FRIEDMAN J. The elements of statistical learning:data mining,inference,and prediction[M]. 2nd ed. New York:Springer, 2009:9-40. DOI: 10.1007/978-0-387-21606-5.
|
[20] |
MARACY M R,KHEIRABADI G R. Development and validation of a postpartum depression risk score in delivered women,Iran[J]. J Res Med Sci,2012,17(11):1067-1071.
|
[21] |
NAKANO M, SOURANDER A, LUNTAMO T,et al. Early risk factors for postpartum depression:a longitudinal Japanese population-based study[J]. J Affect Disord, 2020, 269:148-153. DOI: 10.1016/j.jad.2020.03.026.
|
[22] |
ZABOR E C, REDDY C A, TENDULKAR R D,et al. Logistic regression in clinical studies[J]. Int J Radiat Oncol Biol Phys, 2022, 112(2):271-277. DOI: 10.1016/j.ijrobp.2021.08.007.
|
[23] |
ÇANKAYA S. The effect of psychosocial risk factors on postpartum depression in antenatal period:a prospective study[J]. Arch Psychiatr Nurs, 2020, 34(3):176-183. DOI: 10.1016/j.apnu.2020.04.007.
|
[24] |
TIBSHIRANI R. Regression shrinkage and selection via the lasso:a retrospective[J]. J Royal Stat Soc Ser B Stat Methodol, 2011, 73(3):273-282. DOI: 10.1111/j.1467-9868.2011.00771.x.
|
[25] |
SAFAVIAN S R, LANDGREBE D. A survey of decision tree classifier methodology[J]. IEEE Trans Syst Man Cybern, 1991, 21(3):660-674. DOI: 10.1109/21.97458.
|
[26] |
|
[27] |
|
[28] |
TANG F, ISHWARAN H. Random forest missing data algorithms[J]. Stat Anal Data Min ASA Data Sci J, 2017, 10(6):363-377. DOI: 10.1002/sam.11348.
|
[29] |
|
[30] |
CHEN T Q, GUESTRIN C. XGBoost:a scalable tree boosting system[C]//KDD '16:Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016:785-794. DOI: 10.1145/2939672.2939785.
|
[31] |
HOCHMAN E, FELDMAN B, WEIZMAN A,et al. Development and validation of a machine learning-based postpartum depression prediction model:a nationwide cohort study[J]. Depress Anxiety, 2021, 38(4):400-411. DOI: 10.1002/da.23123.
|
[32] |
WEBB G.I. Naïve Bayes[M/OL]//Sammut C. Webb G. Encyclopedia of Machine Learning and Data Mining. Boston:Springer,2016:1-2.[2022-01-15].
|
[33] |
JIMÉNEZ-SERRANO S, TORTAJADA S, GARCÍA-GÓMEZ J M. A mobile health application to predict postpartum depression based on machine learning[J]. Telemed J E Health, 2015, 21(7):567-574. DOI: 10.1089/tmj.2014.0113.
|
[34] |
BERGER J O. Statistical decision theory and Bayesian analysis [M]. New York:Springer Science & Business Media, 2013:218-247. DOI: 10.1007/978-1-4757-4286-2.
|
[35] |
HEARST M A, DUMAIS S T, OSUNA E,et al. Support vector machines [J]. IEEE Intelligent Systems, 1998, 13(4):18-28. DOI: 10.1109/5254.708428.
|
[36] |
ZHANG W N, LIU H, SILENZIO V M B,et al. Machine learning models for the prediction of postpartum depression:application and comparison based on a cohort study[J]. JMIR Med Inform, 2020, 8(4):e15516. DOI: 10.2196/15516.
|
[37] |
|
[38] |
KRIEGESKORTE N, GOLAN T. Neural network models and deep learning[J]. Curr Biol, 2019, 29(7):R231-236. DOI: 10.1016/j.cub.2019.02.034.
|
[39] |
|
[40] |
RUMELHART D E, HINTON G E, WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088):533-536. DOI: 10.1038/323533a0.
|
[41] |
FATIMA I, ABBASI B, KHAN S,et al. Prediction of postpartum depression using machine learning techniques from social media text [J]. Expert Systems, 2019, 36(4):e12409. DOI: 10.1111/exsy.12409.
|
[42] |
SHIN D, LEE K J, ADELUWA T,et al. Machine learning-based predictive modeling of postpartum depression[J]. J Clin Med, 2020, 9(9):E2899. DOI: 10.3390/jcm9092899.
|
[43] |
ANDERSSON S, BATHULA D R, ILIADIS S I,et al. Predicting women with depressive symptoms postpartum with machine learning methods[J]. Sci Rep, 2021, 11(1):7877. DOI: 10.1038/s41598-021-86368-y.
|
[44] |
HARRELL F E. Regression Modeling Strategies:With Applications to LinearModels,Logistic and Ordinal Regression,and Survival Analysis[M]. 2nd ed. New York:Springer, 2019:63-102. DOI: 10.1007/978-3-319-19425-7.
|
[45] |
PODGORELEC V, KOKOL P, STIGLIC B,et al. Decision trees:an overview and their use in medicine [J]. J Med Syst, 2002, 26(5):445-463. DOI: 10.1023/A:1016409317640.
|
[46] |
YU R J, ABDEL-ATY M. Utilizing support vector machine in real-time crash risk evaluation[J]. Accid Anal Prev, 2013, 51:252-259. DOI: 10.1016/j.aap.2012.11.027.
|
[47] |
RILEY R D, ENSOR J, SNELL K I E,et al. Calculating the sample size required for developing a clinical prediction model[J]. BMJ, 2020, 368:m441. DOI: 10.1136/bmj.m441.
|
[48] |
STEYERBERG E W, HARRELL F E Jr, BORSBOOM G J,et al. Internal validation of predictive models:efficiency of some procedures for logistic regression analysis[J]. J Clin Epidemiol, 2001, 54(8):774-781. DOI: 10.1016/s0895-4356(01)00341-9.
|
[49] |
|