{"title":"Graph Machine Learning With Systematic Hyper-Parameter Selection on Hidden Networks and Mental Health Conditions in the Middle-Aged and Old.","authors":"Kwang-Sig Lee, Byung-Joo Ham","doi":"10.30773/pi.2024.0249","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>It 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.</p><p><strong>Methods: </strong>Data 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.</p><p><strong>Results: </strong>The 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.</p><p><strong>Conclusion: </strong>Improving 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.</p>","PeriodicalId":21164,"journal":{"name":"Psychiatry Investigation","volume":"21 12","pages":"1382-1390"},"PeriodicalIF":1.8000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11704800/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Psychiatry Investigation","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.30773/pi.2024.0249","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/23 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"PSYCHIATRY","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: It 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.
Methods: Data 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.
Results: The 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.
Conclusion: Improving 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.
期刊介绍：
The Psychiatry Investigation is published on the 25th day of every month in English by the Korean Neuropsychiatric Association (KNPA). The Journal covers the whole range of psychiatry and neuroscience. Both basic and clinical contributions are encouraged from all disciplines and research areas relevant to the pathophysiology and management of neuropsychiatric disorders and symptoms, as well as researches related to cross cultural psychiatry and ethnic issues in psychiatry. The Journal publishes editorials, review articles, original articles, brief reports, viewpoints and correspondences. All research articles are peer reviewed. Contributions are accepted for publication on the condition that their substance has not been published or submitted for publication elsewhere. Authors submitting papers to the Journal (serially or otherwise) with a common theme or using data derived from the same sample (or a subset thereof) must send details of all relevant previous publications and simultaneous submissions. The Journal is not responsible for statements made by contributors. Material in the Journal does not necessarily reflect the views of the Editor or of the KNPA. Manuscripts accepted for publication are copy-edited to improve readability and to ensure conformity with house style.
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