Graph Machine Learning With Systematic Hyper-Parameter Selection on Hidden Networks and Mental Health Conditions in the Middle-Aged and Old

• 발행기관: 대한신경정신의학회
• 저자명: Kwang-Sig Lee Byung-Joo Ham
• 간행물 정보: 『Psychiatry Investigation』제21권 제12호, 1382~1390쪽, 전체 9쪽
• 주제분류: 의약학 > 정신과학
• 파일형태: PDF
• 발행일자: 2024.12.31

국문 초록

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.

목차

INTRODUCTION

METHODS

RESULTS

DISCUSSION

REFERENCES

키워드

Graph machine learning Systematic hyper-parameter selection Life satisfaction Subjective class Mental health