Research graph
References from A novel machine learning-based prediction method for patients at risk of developing depressive symptoms using a small data. Local targets link to admitted publications; unresolved targets remain external evidence.
Trends in suicide deaths before and after the COVID-19 outbreak in Korea.
2022 · External reference
Excess suicide attributable to the COVID-19 pandemic and social disparities in South Korea.
2022 · External reference
Machine learning in major depression: From classification to treatment outcome prediction.
10.1111/cns.13048 · 2018 · External reference
Cross-trial prediction of treatment outcome in depression: A machine learning approach
10.1016/s2215-0366(15)00471-x · 2016 · External reference
Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data.
2021 · External reference
Detecting depression and mental illness on social media: an integrative review. Curr Opin Behav Sci
2017 · External reference
Predicting Depression via Social Media.
10.1609/icwsm.v7i1.14432 · 2013 · External reference
Network analysis: An integrative approach to the structure of psychopathology.
10.1146/annurev-clinpsy-050212-185608 · 2013 · External reference
A Network Approach to Psychopathology: New Insights into Clinical Longitudinal Data.
10.1371/journal.pone.0060188 · 2013 · External reference
Network analysis of multivariate data in psychological science
2021 · External reference
State of the aRt personality research: A tutorial on network analysis of personality data in R.
10.1016/j.jrp.2014.07.003 · 2015 · External reference
Psychometric perspectives on diagnostic systems.
10.1002/jclp.20503 · 2008 · External reference
Biomarkers for depression: recent insights, current challenges and future prospects Biomarkers for depression: recent insights, current challenges and future prospects.
External reference
Neuroimaging biomarkers for predicting treatment response and recurrence of major depressive disorder.
2020 · External reference
Prospective biomarkers of major depressive disorder: a systematic review and meta-analysis
10.1038/s41380-019-0585-z · 2020 · External reference
Development and validation of a machine learning-based postpartum depression prediction model: A nationwide cohort study
10.1002/da.23123 · 2021 · External reference
Machine learning in the prediction of postpartum depression: A review
10.1016/j.jad.2022.04.093 · 2022 · External reference
A Comprehensive Survey on Graph Neural Networks
10.1109/tnnls.2020.2978386 · 2021 · External reference
Graph neural networks: A review of methods and applications.
10.1016/j.aiopen.2021.01.001 · 2020 · External reference
A gentle introduction to deep learning for graphs.
10.1016/j.neunet.2020.06.006 · 2020 · External reference
Data Augmentation for Graph Neural Networks.
2021 · External reference
Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017—Conference Track Proceedings.
2017 · External reference
Deeper insights into graph convolutional networks for semi-supervised learning.
2018 · External reference
The effectiveness of data augmentation in image classification using deep learning.
2017 · External reference
Text Data Augmentation for Deep Learning.
10.1186/s40537-021-00492-0 · 2021 · External reference
A survey on Image Data Augmentation for Deep Learning.
10.1186/s40537-019-0197-0 · 2019 · External reference
A Prospective Study on How Symptoms in a Network Predict the Onset of Depression.
10.1159/000442001 · 2016 · External reference
Mindfulness training increases momentary positive emotions and reward experience in adults vulnerable to depression: A randomized controlled trial.
10.1037/a0024595 · 2011 · External reference
