Studies from Korea University Describe New Findings in Machine Learning (Associations of Antidepressant Medication With Its Various Predictors Including Particulate Matter: Machine Learning Analysis Using National Health Insurance Data): Machine Learning

Engineering Business Daily

2022 JUN 29 (NewsRx) -- By a News Reporter-Staff News Editor at Engineering Business Daily -- Investigators discuss new findings in Machine Learning. According to news reporting from Seoul, South Korea, by NewsRx journalists, research stated, “This study uses machine learning and population-based data to analyze major determinants of antidepressant medication including the concentration of particulate matter under 2.5 mu m (PM2.5). Retrospective cohort data came from Korea National Health Insurance Service claims data for 43,251 participants, who were aged 15-79 years, lived in the same districts of Seoul and had no history of antidepressant medication during 2002-2012.”

Financial support for this research came from National Research Foundation of Korea.

The news correspondents obtained a quote from the research from Korea University, “The dependent variable was antidepressant-free months during 2013-2015 and the 30 independent variables for 2012 were included (demographic/socioeconomic information, health information, district-level information including PM2.5). Random forest variable importance, the contribution of a variable for the performance of the model, was used for identifying major predictors of antidepressant-free months. Based on random forest variable importance, the top 15 determinants of antidepressant medication during 2013-2015 included cardiovascular disease (0.0054), age (0.0047), household income (0.0037), gender (0.0027), the district-level proportion of recipients of national basic living security program benefits (0.0019), district-level social satisfaction (0.0013), diabetes mellitus (0.0012), January 2012 PM2.5 (0.0011), district-level street ratio (0.0010), drinker (0.0009), chronic obstructive pulmonary disease (0.0008), district-level economic satisfaction (0.0006), exercise (0.0005), March 2012 PM2.5 (0.0005) and November 2012 PM2.5 (0.0004). Besides these predictors, smoker and district-level deprivation index are found to be influential most widely, given that they ranked within the top 10 most often in sub-group analysis.”

According to the news reporters, the research concluded: “Antidepressant medication has strong associations with neighborhood conditions including socioeconomic satisfaction and the seasonality of particulate matter. Strong interventions for these factors are really needed for the effective management of major depressive disorder.”

This research has been peer-reviewed.

For more information on this research see: Associations of Antidepressant Medication With Its Various Predictors Including Particulate Matter: Machine Learning Analysis Using National Health Insurance Data. Journal of Psychiatric Research, 2022;147:67-78. Journal of Psychiatric Research can be contacted at: Pergamon-elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England. (Elsevier - www.elsevier.com; Journal of Psychiatric Research - http://www.journals.elsevier.com/journal-of-psychiatric-research/)

Our news journalists report that additional information may be obtained by contacting Byung-Joo Ham, Korea University, Dept. of Mental Health, College of Medicine, Seoul, South Korea. Additional authors for this research include Kwang-Sig Lee and Geunyeong Kim.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.jpsychires.2022.01.011. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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