Unveiling the prevalence and risk factors of early stage postpartum depression: a hybrid deep learning approach-Reference-Cited by-同舟云学术

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Unveiling the prevalence and risk factors of early stage postpartum depression: a hybrid deep learning approach

Published:2024-01-25 Issue:26 Volume:83 Page:68281-68315
ISSN:1573-7721
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Lilhore Umesh Kumar,Dalal Surjeet^ORCID,Faujdar Neetu,Simaiya Sarita,Dahiya Mamta,Tomar Shilpi,Hashmi Arshad

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11042-024-18182-3.pdf

Reference41 articles.

1. Kharb S, Joshi A (2023) Multi-omics and machine learning for the prevention and management of female reproductive health. Front Endocrinol 14:358

2. Bartal A, Jagodnik KM, Chan SJ, Babu MS, Dekel S (2023) Identifying women with postdelivery posttraumatic stress disorder using natural language processing of personal childbirth narratives. Am J Obstet Gynecol MFM 5(3):100834

3. Vasha ZN, Sharma B, Esha IJ, Nahian JA, Polin JA (2023) Depression detection in social media comments data using machine learning algorithms. Bull Electr Eng Inform 12(2):987–996

4. Liu H, Dai A, Zhou Z, Xiaowen X, Gao K, Li Q, Shouyu X et al (2023) An optimization for postpartum depression risk assessment and preventive intervention strategy based machine learning approaches. J Affect Disord 328:163–174

5. Xu W, Sampson McClain (2023) Prenatal and Childbirth Risk Factors of Postpartum Pain and Depression: A Machine Learning Approach. Matern Child Health J 27(2):286–296

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