Clinical tools to detect Postpartum Depression based on Machine learning and EEG: A Review
Author:
Affiliation:
1. B. M. S. College of Engineering,Department of Computer science and Engineering,Bengaluru,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10142987/10142969/10142970.pdf?arnumber=10142970
Reference25 articles.
1. Development and validation of a machine learning‐based postpartum depression prediction model: A nationwide cohort study
2. Development and validation of a machine learning algorithm for predicting the risk of postpartum depression among pregnant women
3. Re-examination of perinatal mental health policy frameworks for women signalling distress on the Edinburgh Postnatal Depression Scale (EPDS) completed during their antenatal booking-in consultation: a call for population health intervention
4. Estimation of postpartum depression risk from electronic health records using machine learning
5. Using electronic health records and machine learning to predict postpartum depression;wang;Stud Health Technol Inform,2019
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model;Scientific Reports;2024-02-24
2. Novel Meta Learning Approach for Detecting Postpartum Depression Disorder Using Questionnaire Data;IEEE Access;2024
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