Abstract
Perinatal depression is a disorder that can affect women during pregnancy (prenatal depression) and/or after childbirth (postpartum depression). In this chapter we are aiming to discover whether Artificial Intelligence can predict perinatal depression, and also if it can be used to alleviate it by hel** expand the access to professional treatment.
If evolution really works, how come mothers
have only two hands?
Milton Berle
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ghaedrahmati, M., Kazemi, A., Kheirabadi, G., Ebrahimi, A., Bahrami, M.: Postpartum depression risk factors: a narrative review. J. Educ. Health Promot. 6, 60 (2017)
Lee, D.T., Yip, A.S., Leung, T.Y., Chung, T.K.: Identifying women at risk of postnatal depression: prospective longitudinal study. Hong Kong Med. J. 6, 349–354 (2000)
Davey, H.L., Tough, S.C., Adair, C.E., Benzies, K.M.: Risk factors for sub-clinical and major postpartum depression among a community cohort of Canadian women. Matern. Child Health J. 15, 866–875 (2011)
McCoy, S.J., Beal, J.M., Shipman, S.B., Payton, M.E., Watson, G.H.: Risk factors for postpartum depression: a retrospective investigation at 4-weeks postnatal and review of the literature. J. Am. Osteopath. Assoc. 106, 193–198 (2006)
Kheirabadi, G.R., Maracy, M.R., Barekatain, M., Salehi, M., Sadri, G.H., Kelishadi, M., et al.: Risk factors of postpartum depression in rural areas of Isfahan Province. Iran. Arch. Iran. Med. 12, 461–467 (2009)
Lancaster, C.A., Gold, K.J., Flynn, H.A., Yoo, H., Marcus, S.M., Davis, M.M.: Risk factors for depressive symptoms during pregnancy: a systematic review. Am. J. Obstet. Gynecol. 202, 5–15 (2010)
Bloch, M., Daly, R.C., Rubinow, D.R.: Endocrine factors in the etiology of postpartum depression. Compr. Psychiatry 44, 234–245 (2003)
Butter, M.M., Mott, S.L., Pearlstein, T., Stuart, S., Zlotnick, C., O’Hara, M.W.: Examination of premenstrual symptoms as a risk factor for depression in postpartum women. Arch. Womens Ment. Health 16, 219–225 (2003)
Zinga, D., Phillips, S.D., Born, L.: Postpartum depression: we know the risksm can it be prevented? Rev. Bras. Psiquiatr. 27(2), 56–64 (2005)
Zhang, W., Liu, H., Silenzio, V.M.B., Qiu, P., Gong, W.: Machine learning models for the prediction of postpartum depression: application and comparison based on a cohort study. JMIR Med. Inform. 8(4), e15516 (2020)
Amit, G., Girshovitz, I., Marcus, K., Zhang, Y., Pathak, J., Bar, V., Akiva, P.: Estimation of postpartum depression risk from electronic health records using machine learning. BMC Pregnancy Childbirth 21, 630 (2021)
Blak, B.T., Thompson, M., Dattani, H., Bourke, A.: Generalizability of the health improvement network (THIN) database: demographics, chronic disease prevalence and mortality rates. Inform. Prim. Care 19, 251–255 (2011)
Chen, T., Guestrin, C., XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. New York: Association for Computing Machinery, pp. 785–794 (2016)
Tortajada, S., et al.: Prediction of postpartum depression using multilayer perceptrons and pruning. Methods Inf. Med. 48, 291–298 (2009). https://doi.org/10.3414/ME0562
Wang, S., Pathak, J., Zhang, Y.: Using electronic health records and machine learning to predict postpartum depression. Stud. Health Technol. Inform 264, 888–892 (2019). https://doi.org/10.3233/SHTI190351
Jimenez-Serrano, S., Tortajada, S., Garcia-Gomez, J.: A mobile health application to predict postpartum depression based on machine learning. Telemed. E-Health 21, 567–574 (2015). https://doi.org/10.1089/tmj.2014.0113
Andersson, S., Bathula, D.R., Iliadis, S.I., et al.: Predicting women with depressive symptoms postpartum with machine learning methods. Sci. Rep. 11, 7877 (2021). https://doi.org/10.1038/s41598-021-86368-y
Guintivano, J., et al.: Transcriptome-wide association study for postpartum depression implicated altered B-cell activation and insulin resistance. Mol. Psychiatry (2022). https://doi.org/10.1038/s41380-022-01525-7
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Belciug, S., Iliescu, D. (2023). Perinatal Depression and Artificial Intelligence. In: Pregnancy with Artificial Intelligence. Intelligent Systems Reference Library, vol 234. Springer, Cham. https://doi.org/10.1007/978-3-031-18154-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-18154-2_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-18153-5
Online ISBN: 978-3-031-18154-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)