ArticlePsychiatry investigation2024
Graph Machine Learning With Systematic Hyper-Parameter Selection on Hidden Networks and Mental Health Conditions in the Middle-Aged and Old.
Article in Psychiatry investigation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
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1 citing paper in PubMed.
- Genetic Artificial Intelligence in Gastrointestinal Disease: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
Abstract
objectiveIt takes significant time and energy to collect data on explicit networks. This study used graph machine learning to identify hidden networks and predict mental health conditions in the middle-aged and old.
methodsData came from the Korean Longitudinal Study of Ageing (2016-2018), with 2,000 participants aged 56 or more. The dependent variable was mental disease (no vs. yes) in 2018. Twenty-eight predictors in 2016 were included. Graph machine learning with systematic hyper-parameter selection was conducted.
resultsThe area under the curve was similar across different models in different scenarios. However, sensitivity (93%) was highest for the graph random forest in the scenario of 2,000 participants and the centrality requirement of life satisfaction 90. Based on the graph random forest, top-10 determinants of mental disease were mental disease in previous period (2016), age, income, life satisfaction-health, life satisfaction-overall, subjective health, body mass index, life satisfaction-economic, children alive and health insurance. Especially, life satisfaction-overall was a top-5 determinant in the graph random forest, which considers life satisfaction as an emotional connection and a group interaction.
conclusionImproving an individual's life satisfaction as a personal condition is expected to strengthen the individual's emotional connection as a group interaction, which would reduce the risk of the individual's mental disease in the end. This would bring an important clinical implication for highlighting the importance of a patient's life satisfaction and emotional connection regarding the diagnosis and management of the patient's mental disease.
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