Machine Learning Methods for Predicting Postpartum Depression: Scoping Review

Journals

1. Bilal A, Fransson E, Bränn E, Eriksson A, Zhong M, Gidén K, Elofsson U, Axfors C, Skalkidou A, Papadopoulos F. Predicting perinatal health outcomes using smartphone-based digital phenotyping and machine learning in a prospective Swedish cohort (Mom2B): study protocol. BMJ Open 2022;12(4):e059033 View
2. Zhang H, Yang G, Dong A, Deng X. Prediction Model between Serum Vitamin D and Neurological Deficit in Cerebral Infarction Patients Based on Machine Learning. Computational and Mathematical Methods in Medicine 2022;2022:1 View
3. Bartal A, Jagodnik K, Chan S, Babu M, Dekel S. Identifying women with postdelivery posttraumatic stress disorder using natural language processing of personal childbirth narratives. American Journal of Obstetrics & Gynecology MFM 2023;5(3):100834 View
4. Liu H, Dai A, Zhou Z, Xu X, Gao K, Li Q, Xu S, Feng Y, Chen C, Ge C, Lu Y, Zou J, Wang S. An optimization for postpartum depression risk assessment and preventive intervention strategy based machine learning approaches. Journal of Affective Disorders 2023;328:163 View
5. Liu H, Shi R, Liao R, Liu Y, Che J, Bai Z, Cheng N, Ma H. Machine Learning Based on Event-Related EEG of Sustained Attention Differentiates Adults with Chronic High-Altitude Exposure from Healthy Controls. Brain Sciences 2022;12(12):1677 View
6. Ndaba S. A Review of the Use of R Programming for data Science Research in Botswana. International Journal of Database Management Systems 2023;15(1):1 View
7. Chen L, Hua J, He X. Genetic analysis of cuproptosis subtypes and immunological features in severe influenza. Microbial Pathogenesis 2023;180:106162 View
8. Wang M, Richmond L, Schleider J, Nelson B, Luhmann C. Predicting internalizing symptoms with machine learning: identifying individuals that need care. Journal of American College Health 2023:1 View
9. Hong L, Yang A, Liang Q, He Y, Wang Y, Tao S, Chen L. Wife-Mother Role Conflict at the Critical Child-Rearing Stage: A Machine-Learning Approach to Identify What and How Matters in Maternal Depression Symptoms in China. Prevention Science 2024;25(4):699 View
10. Chen L, Hua J, He X. Identification of cuproptosis-related molecular subtypes as a biomarker for differentiating active from latent tuberculosis in children. BMC Genomics 2023;24(1) View
11. Arora M, Singh J, Singh A. Development of intelligent system based on synthesis of affective signals and deep neural networks to foster mental health of the Indian virtual community. Social Network Analysis and Mining 2024;14(1) View
12. Turchioe M, Hermann A, Benda N. Recentering responsible and explainable artificial intelligence research on patients: implications in perinatal psychiatry. Frontiers in Psychiatry 2024;14 View
13. Lilhore U, Dalal S, Varshney N, Sharma Y, Rao K, Rao V, Alroobaea R, Simaiya S, Margala M, Chakrabarti P. Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model. Scientific Reports 2024;14(1) View
14. Lin S, Wang C, Jiang X, Zhang Q, Luo D, Li J, Li J, Xu J. Using machine learning to develop a five-item short form of the children’s depression inventory. BMC Public Health 2024;24(1) View
15. Naidu P, Ruchitha M, Yaswanth P, Harika B, Prabhu P, Deepthi Sree G. Mental Health Detection using Machine Learning. International Journal of Innovative Science and Research Technology (IJISRT) 2024:760 View
16. Sadjadpour F, Hosseinichimeh N, Abedi V, Soghier L. Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU. Frontiers in Public Health 2024;12 View
17. Guo J, Zhao J, Han P, Wu Y, Zheng K, Huang C, Wang Y, Chen C, Guo Q. Finding the best predictive model for hypertensive depression in older adults based on machine learning and metabolomics research. Frontiers in Psychiatry 2024;15 View